Zomato_Restaurant_Ratings Using Flask.ipynb

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      "metadata": {
        "id": "m1Hfe7Ko5xDi"
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      "source": [
        "# **End-To-End Deployment of Zomato Restaurant Ratings**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rwn0NW5DaETj"
      },
      "source": [
        "# **ABSTRACT**\n",
        "\n",
        "Zomato is one of the best online food delivery apps which gives the users the ratings and the reviews on restaurants all over india.These ratings and the Reviews are considered as one of the most important deciding factors which determine how good a restaurant is. \n",
        "\n",
        "We will therefore use the real time Data set with variuos features a user would look into regarding a restaurant. We will be considering Banglore City in this analysis.\n",
        "\n",
        "Content\n",
        "The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment\n",
        "of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru\n",
        "being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world.\n",
        "\n",
        "With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing\n",
        "day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with\n",
        "established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves.\n",
        "\n",
        "With such an overwhelming demand of restaurants it has therefore become important to study the demography\n",
        "of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food.\n",
        "If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are\n",
        "mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as\n",
        "\n",
        "    • Location of the restaurant\n",
        "    • Approx Price of food\n",
        "    • Theme based restaurant or not\n",
        "    • Which locality of that city serves that cuisines with maximum number of restaurants\n",
        "    • The needs of people who are striving to get the best cuisine of the neighborhood\n",
        "    • Is a particular neighborhood famous for its own kind of food.\n",
        "\n",
        "“Just so that you have a good meal the next time you step out”\n",
        "\n",
        "The data is accurate to that available on the zomato website until 15 March 2019.\n",
        "The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars.\n",
        "\n",
        "Phase I,\n",
        "\n",
        "In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here\n",
        "\n",
        "Phase II,\n",
        "\n",
        "In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted. See section 5 for more details about the variables.\n",
        "\n",
        "Acknowledgements\n",
        "The data scraped was entirely for educational purposes only. Note that I don’t claim any copyright for the data. All copyrights for the data is owned by Zomato Media Pvt. Ltd..\n",
        "\n",
        "            Source: Kaggle"
      ]
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      "source": [
        "**Main Objective:**\n",
        "\n",
        "The main agenda of this project is:\n",
        "\n",
        ">> Perform extensive **Exploratory Data Analysis(EDA)** on the Zomato Dataset.\n",
        "\n",
        ">>Build an appropriate **Machine Learning Model** that will help various Zomato Restaurants to predict their respective Ratings based on certain features\n",
        "\n",
        ">>**DEPLOY** the Machine learning model via **Flask** that can be used to make live predictions of restaurants ratings\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJWIK6_YnS59"
      },
      "source": [
        "\n",
        "#IMPORT THE NECESSARY LIBRARIES\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objs as go\n",
        "import plotly.offline as py\n",
        "import seaborn as sns\n",
        "\n",
        "import matplotlib.ticker as mtick\n",
        "plt.style.use('fivethirtyeight')\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import  ExtraTreesRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FX9eqg0TFkQG"
      },
      "source": [
        "**MOUNT DRIVE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzYuvMndnaSz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "f2f3eb6f-d20a-45bb-e647-82a94dfcc3e3"
      },
      "source": [
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YhladiJjh3Z"
      },
      "source": [
        "#!ls /content/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRxEmhgdFv_P"
      },
      "source": [
        "**LOAD DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGKUYQKdtZWX"
      },
      "source": [
        "\n",
        "data = pd.read_csv('/content/drive/My Drive/zomato.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saGxPAi-08OD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "f95310a9-b55b-4970-f4fb-8e895a036c5d"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>url</th>\n",
              "      <th>address</th>\n",
              "      <th>name</th>\n",
              "      <th>online_order</th>\n",
              "      <th>book_table</th>\n",
              "      <th>rate</th>\n",
              "      <th>votes</th>\n",
              "      <th>phone</th>\n",
              "      <th>location</th>\n",
              "      <th>rest_type</th>\n",
              "      <th>dish_liked</th>\n",
              "      <th>cuisines</th>\n",
              "      <th>approx_cost(for two people)</th>\n",
              "      <th>reviews_list</th>\n",
              "      <th>menu_item</th>\n",
              "      <th>listed_in(type)</th>\n",
              "      <th>listed_in(city)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
              "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.1/5</td>\n",
              "      <td>775</td>\n",
              "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
              "      <td>North Indian, Mughlai, Chinese</td>\n",
              "      <td>800</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
              "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
              "      <td>Spice Elephant</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4.1/5</td>\n",
              "      <td>787</td>\n",
              "      <td>080 41714161</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
              "      <td>Chinese, North Indian, Thai</td>\n",
              "      <td>800</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
              "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
              "      <td>San Churro Cafe</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>3.8/5</td>\n",
              "      <td>918</td>\n",
              "      <td>+91 9663487993</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Cafe, Casual Dining</td>\n",
              "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
              "      <td>Cafe, Mexican, Italian</td>\n",
              "      <td>800</td>\n",
              "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
              "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
              "      <td>Addhuri Udupi Bhojana</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3.7/5</td>\n",
              "      <td>88</td>\n",
              "      <td>+91 9620009302</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Quick Bites</td>\n",
              "      <td>Masala Dosa</td>\n",
              "      <td>South Indian, North Indian</td>\n",
              "      <td>300</td>\n",
              "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
              "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
              "      <td>Grand Village</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3.8/5</td>\n",
              "      <td>166</td>\n",
              "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
              "      <td>Basavanagudi</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Panipuri, Gol Gappe</td>\n",
              "      <td>North Indian, Rajasthani</td>\n",
              "      <td>600</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 url  ... listed_in(city)\n",
              "0  https://www.zomato.com/bangalore/jalsa-banasha...  ...    Banashankari\n",
              "1  https://www.zomato.com/bangalore/spice-elephan...  ...    Banashankari\n",
              "2  https://www.zomato.com/SanchurroBangalore?cont...  ...    Banashankari\n",
              "3  https://www.zomato.com/bangalore/addhuri-udupi...  ...    Banashankari\n",
              "4  https://www.zomato.com/bangalore/grand-village...  ...    Banashankari\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "iGIO3YtM3NLD"
      },
      "source": [
        "## Columns description\n",
        "\n",
        "1. <b>url </B> contains the url of the restaurant in the zomato website\n",
        "\n",
        "2. **address** contains the address of the restaurant in Bengaluru\n",
        "\n",
        "3. **name** contains the name of the restaurant\n",
        "\n",
        "4. **online_order** whether online ordering is available in the restaurant or not\n",
        "\n",
        "5. **book_table** table book option available or not\n",
        "\n",
        "6. **rate** contains the overall rating of the restaurant out of 5\n",
        "\n",
        "7. **votes** contains total number of rating for the restaurant as of the above mentioned date\n",
        "\n",
        "8. **phone** contains the phone number of the restaurant\n",
        "\n",
        "9. **location** contains the neighborhood in which the restaurant is located\n",
        "\n",
        "10. **rest_type** restaurant type\n",
        "\n",
        "11. **dish_liked** dishes people liked in the restaurant\n",
        "\n",
        "12. **cuisines** food styles, separated by comma\n",
        "\n",
        "13. **approx_cost**(for two people) contains the approximate cost of meal for two people\n",
        "\n",
        "14. **reviews_list** list of tuples containing reviews for the restaurant, each tuple\n",
        "\n",
        "15. **menu_item** contains list of menus available in the restaurant\n",
        "\n",
        "16. **listed_in**(type) type of meal\n",
        "\n",
        "17. **listed_in**(**city**) contains the neighborhood in which the restaurant is listed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzBjZU7308Cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e74f256e-ce38-47fb-e778-629ceb98576e"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51717, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lm7MSMLn7O7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "e59ac6da-81c6-43e2-9826-e31b76d0a96e"
      },
      "source": [
        "data.dtypes #checking the data types"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "url                            object\n",
              "address                        object\n",
              "name                           object\n",
              "online_order                   object\n",
              "book_table                     object\n",
              "rate                           object\n",
              "votes                           int64\n",
              "phone                          object\n",
              "location                       object\n",
              "rest_type                      object\n",
              "dish_liked                     object\n",
              "cuisines                       object\n",
              "approx_cost(for two people)    object\n",
              "reviews_list                   object\n",
              "menu_item                      object\n",
              "listed_in(type)                object\n",
              "listed_in(city)                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2Jc2rcR7Rcg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "a59e692b-d189-4ff6-fa9b-43727dfbee49"
      },
      "source": [
        "data.isna().sum() #Checking null values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "url                                0\n",
              "address                            0\n",
              "name                               0\n",
              "online_order                       0\n",
              "book_table                         0\n",
              "rate                            7775\n",
              "votes                              0\n",
              "phone                           1208\n",
              "location                          21\n",
              "rest_type                        227\n",
              "dish_liked                     28078\n",
              "cuisines                          45\n",
              "approx_cost(for two people)      346\n",
              "reviews_list                       0\n",
              "menu_item                          0\n",
              "listed_in(type)                    0\n",
              "listed_in(city)                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8HDwHFnm8P7"
      },
      "source": [
        "#You can use pandas profiling to get an over all overview of the dataset\n",
        "# import pandas_profiling as pf\n",
        "\n",
        "# pf.ProfileReport(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsyWruUv8FGD"
      },
      "source": [
        "#Deleting Unnnecessary Columns\n",
        "df=data.drop(['url','phone'],axis=1) #Dropping the column like \"phone\" and \"url\" and saving the new dataset as \"df\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91UsP-tQGOE-"
      },
      "source": [
        "**Checking for duplicate values**"
      ]
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    {
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      "source": [
        "df.duplicated().sum() "
      ],
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      "outputs": [
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              "43"
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          "metadata": {
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      ]
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      "cell_type": "code",
      "metadata": {
        "id": "ElXzhXcL8MAF"
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      "source": [
        "df.drop_duplicates(inplace=True)"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "JIa8-EYo8LhN",
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          "height": 34
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        "outputId": "3f8be547-531c-4417-ee04-8f0bfaaa2b7a"
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      "source": [
        "df.duplicated().sum()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0"
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          "metadata": {
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          "execution_count": 15
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koofDKxVGXEk"
      },
      "source": [
        "**Drop null values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLfCpb9Q7UEm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "61684b1c-bfd9-4b93-eab7-9731220398b2"
      },
      "source": [
        "#Remove the NaN values from the dataset\n",
        "df.dropna(how='any',inplace=True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "address                        0\n",
              "name                           0\n",
              "online_order                   0\n",
              "book_table                     0\n",
              "rate                           0\n",
              "votes                          0\n",
              "location                       0\n",
              "rest_type                      0\n",
              "dish_liked                     0\n",
              "cuisines                       0\n",
              "approx_cost(for two people)    0\n",
              "reviews_list                   0\n",
              "menu_item                      0\n",
              "listed_in(type)                0\n",
              "listed_in(city)                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBXoRubQGjHu"
      },
      "source": [
        "**Renaming columes appropriately**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2k3t1cI7gvz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "b2858209-ffae-47de-efb0-a360b496464f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
              "       'location', 'rest_type', 'dish_liked', 'cuisines',\n",
              "       'approx_cost(for two people)', 'reviews_list', 'menu_item',\n",
              "       'listed_in(type)', 'listed_in(city)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Vcucfr9yqP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "150363fc-b3ca-492d-be7d-a3cfaf2534d2"
      },
      "source": [
        "df = df.rename(columns={'approx_cost(for two people)':'cost','listed_in(type)':'type',\n",
        "                                  'listed_in(city)':'city'})\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
              "       'location', 'rest_type', 'dish_liked', 'cuisines', 'cost',\n",
              "       'reviews_list', 'menu_item', 'type', 'city'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eq1fmTjQ90Yq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "8540f3b5-7b11-48a0-b76c-6af2b2bd7414"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>address</th>\n",
              "      <th>name</th>\n",
              "      <th>online_order</th>\n",
              "      <th>book_table</th>\n",
              "      <th>rate</th>\n",
              "      <th>votes</th>\n",
              "      <th>location</th>\n",
              "      <th>rest_type</th>\n",
              "      <th>dish_liked</th>\n",
              "      <th>cuisines</th>\n",
              "      <th>cost</th>\n",
              "      <th>reviews_list</th>\n",
              "      <th>menu_item</th>\n",
              "      <th>type</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.1/5</td>\n",
              "      <td>775</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
              "      <td>North Indian, Mughlai, Chinese</td>\n",
              "      <td>800</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
              "      <td>Spice Elephant</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4.1/5</td>\n",
              "      <td>787</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
              "      <td>Chinese, North Indian, Thai</td>\n",
              "      <td>800</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
              "      <td>San Churro Cafe</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>3.8/5</td>\n",
              "      <td>918</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Cafe, Casual Dining</td>\n",
              "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
              "      <td>Cafe, Mexican, Italian</td>\n",
              "      <td>800</td>\n",
              "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
              "      <td>Addhuri Udupi Bhojana</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3.7/5</td>\n",
              "      <td>88</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Quick Bites</td>\n",
              "      <td>Masala Dosa</td>\n",
              "      <td>South Indian, North Indian</td>\n",
              "      <td>300</td>\n",
              "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
              "      <td>Grand Village</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3.8/5</td>\n",
              "      <td>166</td>\n",
              "      <td>Basavanagudi</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Panipuri, Gol Gappe</td>\n",
              "      <td>North Indian, Rajasthani</td>\n",
              "      <td>600</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             address  ...          city\n",
              "0  942, 21st Main Road, 2nd Stage, Banashankari, ...  ...  Banashankari\n",
              "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...  ...  Banashankari\n",
              "2  1112, Next to KIMS Medical College, 17th Cross...  ...  Banashankari\n",
              "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  ...  Banashankari\n",
              "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...  ...  Banashankari\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eMLZMTujGu-Y"
      },
      "source": [
        "**Cleaning the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SA7rcyph93Vr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "7458e4e1-f382-46fc-a126-351fd09150f0"
      },
      "source": [
        "df['cost'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['800', '300', '600', '700', '550', '500', '450', '650', '400',\n",
              "       '750', '200', '850', '1,200', '150', '350', '250', '1,500',\n",
              "       '1,300', '1,000', '100', '900', '1,100', '1,600', '950', '230',\n",
              "       '1,700', '1,400', '1,350', '2,200', '2,000', '1,800', '1,900',\n",
              "       '180', '330', '2,500', '2,100', '3,000', '2,800', '3,400', '40',\n",
              "       '1,250', '3,500', '4,000', '2,400', '1,450', '3,200', '6,000',\n",
              "       '1,050', '4,100', '2,300', '120', '2,600', '5,000', '3,700',\n",
              "       '1,650', '2,700', '4,500'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtRvCpoUHaqr"
      },
      "source": [
        "**replacing the \",\" with nothing and converting the results to float**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spH8O4KI96JO"
      },
      "source": [
        "#zomato['cost'] = zomato['cost'].astype(str) #Changing the cost to string\n",
        "df['cost'] = df['cost'].apply(lambda x: x.replace(',','')) #Using lambda function to replace ',' from cost\n",
        "df['cost'] = df['cost'].astype(float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWHX4KfE98NW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "395bc95c-5558-4afd-a8ea-cf6e988b34b4"
      },
      "source": [
        "print(df['cost'].unique())\n",
        "\n",
        "print('---'*10)\n",
        "\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 800.  300.  600.  700.  550.  500.  450.  650.  400.  750.  200.  850.\n",
            " 1200.  150.  350.  250. 1500. 1300. 1000.  100.  900. 1100. 1600.  950.\n",
            "  230. 1700. 1400. 1350. 2200. 2000. 1800. 1900.  180.  330. 2500. 2100.\n",
            " 3000. 2800. 3400.   40. 1250. 3500. 4000. 2400. 1450. 3200. 6000. 1050.\n",
            " 4100. 2300.  120. 2600. 5000. 3700. 1650. 2700. 4500.]\n",
            "------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "address          object\n",
              "name             object\n",
              "online_order     object\n",
              "book_table       object\n",
              "rate             object\n",
              "votes             int64\n",
              "location         object\n",
              "rest_type        object\n",
              "dish_liked       object\n",
              "cuisines         object\n",
              "cost            float64\n",
              "reviews_list     object\n",
              "menu_item        object\n",
              "type             object\n",
              "city             object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMl98lIQ-CxV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "195fcb83-29ef-4509-8a03-27196b578479"
      },
      "source": [
        "#Reading uninque values from the Rate column\n",
        "df['rate'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',\n",
              "       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',\n",
              "       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',\n",
              "       '3.4/5', '2.7/5', '4.7/5', 'NEW', '2.4/5', '2.2/5', '2.3/5',\n",
              "       '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5',\n",
              "       '2.7 /5', '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5',\n",
              "       '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5',\n",
              "       '3.3 /5', '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5',\n",
              "       '3.5 /5', '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',\n",
              "       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uj25hzkj-Fio"
      },
      "source": [
        "df = df.loc[df.rate !='NEW'] #getting rid of \"NEW\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MP-iu9RR-K3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "e603f078-0a78-410d-c7dd-de8e5d40a24a"
      },
      "source": [
        "df['rate'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',\n",
              "       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',\n",
              "       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',\n",
              "       '3.4/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5', '4.8/5',\n",
              "       '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5', '2.7 /5',\n",
              "       '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5', '4.4 /5',\n",
              "       '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5', '3.3 /5',\n",
              "       '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5', '3.5 /5',\n",
              "       '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5', '2.1 /5',\n",
              "       '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYrpzL_I-87r"
      },
      "source": [
        "#Removing '/5' from Rates\n",
        "\n",
        "df['rate'] = df['rate'].apply(lambda x: x.replace('/5',''))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LY94MGcN_HVz"
      },
      "source": [
        "<br>\n",
        "\n",
        "# Visualisations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvDnlKmFIA9J"
      },
      "source": [
        "Most famous restaurants chains in Bangaluru"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7BpfKCe_Apk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "6ce82a8a-f888-4089-eec6-5d529d06eb6f"
      },
      "source": [
        "plt.figure(figsize=(17,10))\n",
        "chains=df['name'].value_counts()[:20]\n",
        "sns.barplot(x=chains,y=chains.index,palette='deep')\n",
        "plt.title(\"Most famous restaurants chains in Bangaluru\")\n",
        "plt.xlabel(\"Number of outlets\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gES28ir2If3I"
      },
      "source": [
        "<br>\n",
        "\n",
        "Whether restaurant offer Table booking or not"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsnPTEKZAf2g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "f9dd609f-bc09-4683-a28b-c0bc1a6754be"
      },
      "source": [
        "x=df['book_table'].value_counts()\n",
        "colors = ['#800080', '#0000A0']\n",
        "\n",
        "trace=go.Pie(labels=x.index,values=x,textinfo=\"value\",\n",
        "            marker=dict(colors=colors, \n",
        "                           line=dict(color='#001000', width=2)))\n",
        "layout=go.Layout(title=\"Table booking\",width=600,height=600)\n",
        "fig=go.Figure(data=[trace],layout=layout)\n",
        "py.iplot(fig, filename='pie_chart_subplots')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"d19e4379-5617-4469-8695-6f82afd10ed9\" class=\"plotly-graph-div\" style=\"height:600px; width:600px;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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              "                    Plotly.newPlot(\n",
              "                        'd19e4379-5617-4469-8695-6f82afd10ed9',\n",
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRWv05H5DSwv"
      },
      "source": [
        "**Insight**\n",
        "\n",
        "Most of the Restaurants do not offer table booking"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLzdOr1RIcBU"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "Whether Restaurants deliver online or Not"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHlO3ZYWA0a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "29f8de6a-c806-4880-fa82-77a9eead5977"
      },
      "source": [
        "#Restaurants delivering Online or not\n",
        "sns.countplot(df['online_order'])\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(10,10)\n",
        "plt.title('Whether Restaurants deliver online or Not')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otl47PIGCpa7"
      },
      "source": [
        "**Insight:**\n",
        "\n",
        "Most Restaurants offer option for online order and delivery\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irSk9nGNElR9"
      },
      "source": [
        "<br>\n",
        "\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RTihUbQGbtD"
      },
      "source": [
        "## **Rating Distributions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tATFlDCCqMP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "11158e40-320d-46a9-a807-c787a3eaf16f"
      },
      "source": [
        "#How ratings are distributed\n",
        "plt.figure(figsize=(9,7))\n",
        "\n",
        "sns.distplot(df['rate'],bins=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f714ef32828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ucgyP7xC3Te"
      },
      "source": [
        "\n",
        "\n",
        "*  **Insight**\n",
        "\n",
        "  We can infer from above that most of the ratings are within 3.5 and 4.5\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlR7V3e-FC9n"
      },
      "source": [
        "# #Distribution of the cost Vs ratings in parallel with online order\n",
        "# plt.figure(figsize=(10,7))\n",
        "# sns.scatterplot(x=\"rate\",y='cost',hue='online_order',data=df)\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBbI9hYjKH96"
      },
      "source": [
        "**Count of ratings as between \"1 and 2\", \"2 and 3\", \"3 and 4\", and \"4 and 5\"** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4Ep9S4AIxzg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "9102aeea-2426-4710-b63b-a7d2c9eb441e"
      },
      "source": [
        "df['rate'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',\n",
              "       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',\n",
              "       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',\n",
              "       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',\n",
              "       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',\n",
              "       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',\n",
              "       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',\n",
              "       '2.0 ', '1.8 '], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcVA7_qDI4PB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "31d89ef6-2575-469c-c013-e377e59315bc"
      },
      "source": [
        "df['rate'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'1.8'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ym4IkBmKUTi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8e0d39fe-70bf-4148-ddd7-c8479aa78feb"
      },
      "source": [
        "df['rate'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'4.9 '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94rfzI0KKXpQ"
      },
      "source": [
        "df['rate']=df['rate'].astype(float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WPZsXG3Noee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4cb82853-a10f-4e48-c23a-77fb1c7538d3"
      },
      "source": [
        "((df['rate']>=1) & (df['rate']<2)).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkhwCHIdKc7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fdbf286a-57fc-4529-c29c-fe0d335b7091"
      },
      "source": [
        "((df['rate']>=2) & (df['rate']<3)).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1179"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoD65fBeMHfM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "20cc8d3f-4d4c-4cf0-8163-8ae7adb4bb5c"
      },
      "source": [
        "((df['rate']>=3) & (df['rate']<4)).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10153"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_MF1kQINZlf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3c7a8142-ec8a-4ac9-84c2-a3ee51852839"
      },
      "source": [
        "(df['rate']>=4).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11911"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhzrP_vaK7VX"
      },
      "source": [
        "<br>\n",
        "\n",
        "**Plotting the counts with the help of pie chart**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0IXasKVPfJc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "da365adf-478f-4a7e-fab0-569bea2d4d68"
      },
      "source": [
        "slices=[((df['rate']>=1) & (df['rate']<2)).sum(),\n",
        "        ((df['rate']>=2) & (df['rate']<3)).sum(),\n",
        "        ((df['rate']>=3) & (df['rate']<4)).sum(),\n",
        "        (df['rate']>=4).sum()\n",
        "        ]\n",
        "\n",
        "labels=['1<rate<2','2<rate<3','3<rate<4','>4']\n",
        "colors = ['#ff3333','#c2c2d6','#6699ff']\n",
        "plt.pie(slices,colors=colors, labels=labels, autopct='%1.0f%%', pctdistance=.5, labeldistance=1.2,shadow=True)\n",
        "fig = plt.gcf()\n",
        "plt.title(\"Percentage of Restaurants according to their ratings\")\n",
        "\n",
        "fig.set_size_inches(10,10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1I22UbW0PbCU"
      },
      "source": [
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKmZ6ut6qvVr"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWgbKtTQEcNS"
      },
      "source": [
        "## **Services Types**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WQyTwIXCxQM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "36ed67bf-ef44-4735-fa21-331bce9a4592"
      },
      "source": [
        "#Types of Services\n",
        "\n",
        "sns.countplot(df['type']).set_xticklabels(sns.countplot(df['type']).get_xticklabels(), rotation=90, ha=\"right\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(12,12)\n",
        "plt.title('Type of Service')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Type of Service')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weBwkJyDU0ty"
      },
      "source": [
        "Here the two main service types are **Delivery** and **Dine-out**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGMNCnXsVAaH"
      },
      "source": [
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xu2m-CDmOXDY"
      },
      "source": [
        "## **Distribution of Cost of Food for two People**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyvG6ysuc9AE"
      },
      "source": [
        "from plotly.offline import iplot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5xNZxDidNs7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "377f4b17-9d81-477d-f4da-b06763302733"
      },
      "source": [
        "trace0=go.Box(y=df['cost'],name=\"accepting online orders\",\n",
        "              marker = dict(\n",
        "        color = 'rgb(113, 10, 100)',\n",
        "    ))\n",
        "data=[trace0]\n",
        "layout=go.Layout(title=\"Box plot of approximate cost\",width=800,height=800,yaxis=dict(title=\"Price\"))\n",
        "fig=go.Figure(data=data,layout=layout)\n",
        "py.iplot(fig)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"d1256214-3c7e-4d5e-8146-99c0ce66b1bf\" class=\"plotly-graph-div\" style=\"height:800px; width:800px;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"d1256214-3c7e-4d5e-8146-99c0ce66b1bf\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'd1256214-3c7e-4d5e-8146-99c0ce66b1bf',\n",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Box plot of approximate cost\"}, \"width\": 800, \"yaxis\": {\"title\": {\"text\": \"Price\"}}},\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4zJWhCbLk6w"
      },
      "source": [
        "**Distribution of charges**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QP8phgpqNjdG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "3a9b150b-1263-49c6-8b6a-e752b85a7674"
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "sns.distplot(df['cost'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08lkpOlGVMwe"
      },
      "source": [
        "## **Most Liked Dishes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpEEWZkeUYWr"
      },
      "source": [
        "#re=regular expression (use for splitting words)\n",
        "\n",
        "import re\n",
        "\n",
        "df.index=range(df.shape[0])\n",
        "likes=[]\n",
        "for i in range(df.shape[0]):\n",
        "    array_split=re.split(',',df['dish_liked'][i])\n",
        "    for item in array_split:\n",
        "        likes.append(item)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orPvuZjiW0MD"
      },
      "source": [
        "df.index=range(df.shape[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD-uQwFCW1UY"
      },
      "source": [
        "df.index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qILhSkOXW5tb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "fd15a0df-ca70-4345-90ba-ae5ea7ec0f6d"
      },
      "source": [
        "print(\"Count of Most liked dishes in Bangalore\")\n",
        "favourite_food = pd.Series(likes).value_counts()\n",
        "favourite_food.head(30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Count of Most liked dishes in Bangalore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Pasta              2692\n",
              " Pizza              1915\n",
              " Cocktails          1880\n",
              " Burgers            1736\n",
              " Mocktails          1623\n",
              " Biryani            1307\n",
              " Sandwiches         1287\n",
              "Burgers             1256\n",
              " Coffee             1184\n",
              " Nachos             1170\n",
              " Fish               1116\n",
              " Paratha            1107\n",
              " Salads             1055\n",
              " Chicken Biryani    1004\n",
              "Cocktails            891\n",
              " Fries               876\n",
              " Noodles             854\n",
              " Beer                835\n",
              " Mutton Biryani      832\n",
              " Tea                 819\n",
              "Coffee               801\n",
              " Sandwich            788\n",
              " Butter Chicken      782\n",
              " Thali               770\n",
              "Biryani              749\n",
              "Pizza                747\n",
              " Roti                729\n",
              " Brownie             726\n",
              " Salad               677\n",
              " Hot Chocolate       672\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5QGQc9OX5h_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "outputId": "0ae4c09e-6382-4c2f-9d32-af0954adc264"
      },
      "source": [
        "ax = favourite_food.nlargest(n=20, keep='first').plot(kind='bar',figsize=(18,10),title = 'Top 30 Favourite Food counts ')\n",
        "\n",
        "for i in ax.patches:\n",
        "    ax.annotate(str(i.get_height()), (i.get_x() * 1.005, i.get_height() * 1.005))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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SAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBaBEkAAAAA1CJIAgAAAKAWQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBalhoklVK2KKXcXkp5uJTyUCnl1Gb72aWUmaWU+5vbQe2O+WwpZUYp5fellHe3az+g2TajlDJ+xZQEAAAAwIrQo0af+Uk+VVXVtFLKuknuLaXc2tx3UVVVF7TvXEoZkuT9Sd6epF+S20opb2vuvjjJfkn+kuSeUsqNVVU9vDwKAQAAAGDFWmqQVFXVrCSzmp9fKKU8kqT/GxxyWJKrq6r6R5I/llJmJNm1uW9GVVV/SJJSytXNvoIkAAAAgFVAh9ZIKqUMSLJTkrubTaeUUh4opVxWSundbOuf5M/tDvtLs+312gEAAABYBZSqqup1LOUtSSYnObeqqutKKZsm+WuSKsk5SfpWVfXBUso3ktxVVdX3m8dNSDKpeZoDqqr6ULP9uCS7VVV1ysJrPPfcc22DmT59eocKGdraq0P9/1n3tLzUqdcDAAAAWNEGDRrU9nn99dcvi++vs0ZSSilrJbk2yVVVVV2XJFVVPdVu/6VJftL8OjPJFu0O37zZljdof8OB19L6uqdaITo8vi40ffr0VWq8HaW+VV93r1F9q77uXmN3ry/p/jWqb9XX3WtU36qvu9fY3etLun+N6us8dd7aVpJMSPJIVVUXtmvv267be5P8tvn5xiTvL6WsXUrZOsmgJL9Kck+SQaWUrUspPdNYkPvG5VMGAAAAACtanRlJI5Mcl+TBUsr9zbbPJTm6lLJjGo+2PZbk5CSpquqhUsr/pLGI9vwk46qqWpAkpZRTkvw0yZpJLquq6qHlWAsAAAAAK1Cdt7a1JnnNM3FJJr7BMecmOXcJ7RPf6DgAAAAAVl4demsbAAAAAKsvQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBaBEkAAAAA1CJIAgAAAKAWQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBaBEkAAAAA1CJIAgAAAKAWQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBaBEkAAAAA1CJIAgAAAKAWQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWpYaJJVStiil3F5KebiU8lAp5dRm+4allFtLKdObf/ZutpdSytdLKTNKKQ+UUt7Z7lwfaPafXkr5wIorCwAAAIDlrc6MpPlJPlVV1ZAkw5KMK6UMSTI+yc+rqhqU5OfN70lyYJJBze3DSb6ZNIKnJGcl2S3JrknOWhg+AQAAALDyW2qQVFXVrKqqpjU/v5DkkST9kxyW5IpmtyuSjGp+PizJlVXDXUk2KKX0TfLuJLdWVfW3qqqeTXJrkgOWazUAAAAArDAdWiOplDIgyU5J7k6yaVVVs5q7nkyyafNz/yR/bnfYX5ptr9cOAAAAwCqgVFVVr2Mpb0kyOcm5VVVdV0qZU1XVBu32P1tVVe9Syk+SfKWqqtZm+8+TnJFkryRvqqrqS832Lyb5e1VVFyw8x3PPPdc2mOnTp3eokKGtvTrU/591T8tLnXo9AAAAgBVt0KBBbZ/XX3/9svj+HnVOUkpZK8m1Sa6qquq6ZvNTpZS+VVXNaj669nSzfWaSLdodvnmzbWYaYVL79jvqDLyW1pkd6/9P6vD4utD06dNXqfF2lPpWfd29RvWt+rp7jd29vqT716i+VV93r1F9q77uXmN3ry/p/jWqr/PUeWtbSTIhySNVVV3YbteNSRa+ee0DSW5o13588+1tw5I813wE7qdJ9i+l9G4usr1/sw0AAACAVUCdGUkjkxyX5MFSyv3Nts8l+UqS/ymljE3yeJKjmvsmJjkoyYwkLyU5MUmqqvpbKeWcJPc0+/1bVVV/Wy5VAAAAALDCLTVIaq519Jpn4pr2XUL/Ksm41znXZUku68gAAQAAAFg5dOitbQAAAACsvgRJAAAAANQiSFrFjBs3LgMHDszw4cPb2h588MHst99+GTFiRN73vvfl+eefT5L87W9/yyGHHJI99tgjp59++iLnOfjgg7PLLrukpaUlLS0teeaZZzq1DgAAAGDVI0haxYwZMybXXHPNIm2f+MQnctZZZ2Xq1Kk55JBD8vWvfz1Jsvbaa+fzn/98Tj311CWe69JLL01ra2taW1vTp0+fFT52AAAAYNUmSFrFjBw5Mr17916k7dFHH83IkSOTJHvvvXduuummJMk666yT4cOHp2fPnp0+TgAAAKD7ESR1A9tss01uvvnmJMn111+fmTNn1jpu3LhxaWlpyfnnn5/Gy/YAAAAAXp8gqRv4xje+kQkTJmTPPffMiy++mLXWWmupx1x66aWZOnVqJk2alF/+8pe5+uqrO2GkAAAAwKpMkNQNvO1tb8uPf/zjTJ48OUcccUS23nrrpR7Tr1+/JMm6666bI444ItOmTVvRwwQAAABWcYKkbmDhG9deffXVfPWrX82JJ574hv3nz5+f2bNnJ0leeeWV/PSnP8222267wsdZx5LeSvfAAw/kXe96V1paWrLXXnvl3nvvTZI899xzed/73pcxY8Zk2LBh+f73v992zA9+8IO8853vzDvf+c784Ac/6PQ6AAAAoDvq0dUDoGPGjh2b1tbWzJ49O0OGDMn48eMzd+7cfOc730mSHHrooTn22GPb+m+//fZ57rnnsmDBgtx888257rrrssUWW+Twww/PK6+8kldffTV77rlnPvCBD3RVSYsYM2ZMTjrppHz0ox9tazvrrLNyxhlnZL/99svPfvaznHnmmbn55pvzne98J9tss02+9KUvpXfv3tlll11y1FFHZe7cuTnvvPNyxx13pJSSPffcMwcddFA22GCDLqwMAAAAVn2CpFXMhAkTltjePnhp78EHH8z06dMzaNCgRdonT5683Me2PIwcOTKPP/74Im2llLzwwgtJkueffz59+/Zta3/xxRdTVVVefPHF9O7dOz169MjPf/7z7L333m1vt9t7771z22235YgjjujcYgAAAKCbESStIja4vN6b2JasV9La8ePnnNj/n7jm8vPv//7vGT16dL74xS/m1VdfzU9/+tMkyUknnZSjjz46Bx54YF5++eVcdtllWWONNTJr1qz07/9/Y+/Xr19mzZrVVcMHAACAbsMaSaz0JkyYkHPPPTcPPfRQvvzlL+fjH/94kuQXv/hFtt9++0yaNCl33nlnTj/99Dz//PNdPFoAAADovgRJrPSuvvrqvOc970mSjBo1qu0Nc1dddVUOPfTQlFLy1re+NVtttVWmT5+evn37ZubM/5uB9cQTT7Q9DgcAAAAsO0ESK73NNtssra2tSZIpU6bkrW99a5Jk8803b1vr6emnn86MGTMyYMCA7LvvvvnFL36ROXPmZM6cOfnFL36Rfffdt8vGDwAAAN2FNZJYqSzprXRf+9rXMn78+MyfPz9vetOb8rWvfS1Jcvrpp+djH/tYrrnmmvTs2TNnn312Ntpoo7Z9e++9d5LkM5/5TNvC2wAAAMCyEySxUnm9t9It6S1zffv2zY9//OMlvpXuuOOOy3HHHbdCxggAAACrK4+2AQAAAFCLGUmsNDa4fObSOy1Rr6S148fOObH/Ml4PAAAAVk9mJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCTrZuHHjMnDgwAwfPryt7cQTT0xLS0taWlqy/fbbp6WlJUly7733ZsyYMWlpacnIkSNz0003tR0zZ86cHH/88Rk6dGh23XXX/OpXv+r0WgAAAFi99OjqAcDqZsyYMTnppJPy0Y9+tK3t8ssvb/v8+c9/Puutt16SZNttt82VV16ZbbfdNk8++WRaWlpy4IEHpkePHhk/fnze9a535corr8y8efPy0ksvdXotAAAArF7MSIJONnLkyPTu3XuJ+6qqyvXXX58jjjgiSdKrV6/06NHIe19++eWUUpIkzz33XKZOnZrjjjsuSdKzZ89ssMEGnTB6AAAAVmeCJFiJTJ06NX369Mm//Mu/tLX99re/zbBhwzJy5MhceOGF6dGjRx5//PFsvPHG+djHPpbdd989H//4xzN37twuHDkAAACrA0ESrESuvfbajB49epG27bbbLnfddVd+8Ytf5KKLLsrLL7+cBQsW5De/+U3Gjh2bO++8M7169cpFF13URaMGAABgdSFIgpXE/Pnzc9NNN+Xwww9f4v7BgwdnnXXWySOPPJJ+/fqlX79+2WWXXZIkhx12WB544IHOHC4AAACrIUESrCTuuOOODBo0KP37929re+yxxzJ//vwkyZ/+9KdMnz49W265ZTbddNNsvvnmmT59epJk8uTJGTx4cJeMGwAAgNWHt7ZBJxs7dmxaW1sze/bsDBkyJOPHj8/xxx+fa6+9tm2R7YXuuuuunHfeeVlnnXWyxhpr5IILLshGG22UJDnvvPNy0kknZd68eRkwYEAuueSSrigHAACA1YggCTrZhAkTltj+zW9+8zVt73//+7Pzzjtn0KBBr9n3jne8I3fcccfyHh4AAAC8Lo+2AQAAAFCLGUnQSTa4fOYyHtkrae34sXNO7L/0TgAAANABZiQBAAAAUIsgCQAAAIBaBEkAAAAA1CJIAgAAAKAWQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgClrtx48Zl4MCBGT58+CLt3/rWtzJ06NAMGzYsZ555ZpLk9ttvz3HHHZcRI0Zkzz33zOTJk9v6jx49OiNHjsywYcNy2mmnZcGCBZ1aBwAAAIvq0dUDALqfMWPG5KSTTspHP/rRtrYpU6Zk4sSJaW1tzdprr51nnnkmSbLhhhvmwgsvzIgRI/Lwww9n9OjReeSRR5Ikl19+edZbb71UVZXjjz8+119/fUaPHt0lNQEAAGBGErACjBw5Mr17916k7bLLLstpp52WtddeO0nSp0+fJMkOO+zQ9nnbbbfN3//+9/zjH/9Ikqy33npJkvnz52fevHkppXRWCQAAACyBIAnoFDNmzMjUqVOz77775qCDDsq0adNe0+fGG2/MDjvs0BY2Jcnhhx+egQMHZt11181hhx3WmUMGAABgMYIkoFMsWLAgzz77bG677bacc845OeGEE1JVVdv+Rx55JGeddVb+8z//c/6kMO8AACAASURBVJHjrrvuuvz+97/PP/7xj0yZMqWzhw0AAEA7giSgU/Tr1y+HHnpoSinZeeeds8Yaa2T27NlJkqeeeirHHnts/uu//itbb731a45905velIMOOigTJ07s7GEDAADQjiAJ6BQHH3xw7rzzziSNx9xeeeWVbLTRRpkzZ05OO+20nHXWWRk2bFhb/xdffDFPPvlkksYaST/72c8yaNCgLhk7AAAADd7aBix3Y8eOTWtra2bPnp0hQ4Zk/PjxOfbYY3PKKadk+PDhWWuttXLJJZeklJJLL700f/7zn3P++efn/PPPT5L8+Mc/TlVVOfroo/OPf/wjVVWlpaUlH/zgB7u4MgAAgNWbIAlY7iZMmLDE9m9/+9uvaTv99NMzatSoJc42uv3225f72AAAAFh2giRgudng8pnLeGSvpLXjx845sf8yXg8AAIBlYY0kAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiQAAAAAahEkAQAAAFCLIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBalhoklVIuK6U8XUr5bbu2s0spM0sp9ze3g9rt+2wpZUYp5fellHe3az+g2TajlDJ++ZcCAAAAwIpUZ0bSd5McsIT2i6qq2rG5TUySUsqQJO9P8vbmMZeUUtYspayZ5OIkByYZkuToZl8AAAAAVhE9ltahqqoppZQBNc93WJKrq6r6R5I/llJmJNm1uW9GVVV/SJJSytXNvg93eMQAAAAAdIl/Zo2kU0opDzQffevdbOuf5M/t+vyl2fZ67QAAAACsIkpVVUvv1JiR9JOqqrZrft80yV+TVEnOSdK3qqoPllK+keSuqqq+3+w3Icmk5mkOqKrqQ83245LsVlXVKe2v89xzz7UNZvr06R0qZGhrrw71/2fd0/JSp16vs+tLun+N6lv+VocaAQAAurNBgwa1fV5//fXL4vuX+mjbklRV9dTCz6WUS5P8pPl1ZpIt2nXdvNmWN2hfovYDr6X1DU+33HV4fP+sTq4v6f41qm8FWB1qXAbTp09fZca6LLp7fUn3r7G715d0/xrVt+rr7jWqb9XX3Wvs7vUl3b9G9XWeZXq0rZTSt93X9yZZ+Ea3G5O8v5Sydill6ySDkvwqyT1JBpVSti6l9ExjQe4bl33YAAAAAHS2pc5IKqX8MMleSTYupfwlyVlJ9iql7JjGo22PJTk5SaqqeqiU8j9pLKI9P8m4qqoWNM9zSpKfJlkzyWVVVT203KsBAAAAYIWp89a2o5fQPOEN+p+b5NwltE9MMrFDowMAAABgpfHPvLUNAAAAgNWIIAkAAACAWgRJAAAAANQiSAIAAACgFkESAAAAALUIkgAAAACoRZAEAAAAQC2CJAAAAABqESQBAAAAUIsgCQAAAIBaBEkAAAAA1CJIAgAAAKAWQRIAAAAAtQiSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJAAAAgFoESQAAAADUIkgCAAAAoBZBEgAAAAC1CJIAAAAAqEWQBAAAAEAtgiSADho3blwGDhyY4cOHt7V96UtfyogRI9LS0pL3vve9mTVrVpKkqqpccMEF2WmnnTJixIjcf//9SZIpU6akpaWlbdt0003zk5/8pEvqAQAAqEuQBNBBY8aMyTXXXLNI2yc+8YlMnTo1ra2tefe7353zzz8/SXLrrbfmT3/6U6ZNm5avfe1r+dSnPpUk2WOPPdLa2prW1tbcdNNNefOb35x99tmn02sBAADoCEESQAeNHDkyvXv3XqRtvfXWa/v80ksvpZSSJJk4cWIOPvjglFIydOjQPPfcc3nyyScXOfaGG27Ifvvtl169eq34wQMAAPwTBEkAy8k555yTt7/97fnRj36Uz33uc0mSWbNmZdNNN23r069fv7bH3ha69tprM3r06E4dKwAAwLIQJAEsJ1/84hfz0EMP5cgjj8y3v/3tWsc8+eSTefjhh7Pvvvuu4NF1zJLWgfriF7+YoUOHZsSIETnmmGMyZ86cJMn8+fPzkY98JCNGjMiuu+6aCy+8MEkyffr0RdaB2mKLLXLJJZd0ST0AAMDyIUgCWM6OPPLI3HTTTUmSvn375qmnnmrb98QTT6Rv375t33/84x/nkEMOyVprrdXp43wjS1oHau+9984vf/nLTJ06NQMHDsxFF12UJLntttsyb968TJ06NXfccUcuv/zyPP744xk0aFDbOlCTJ0/Om9/85hxyyCFdUQ4AALCcCJIAloNHH3207fPEiRMzaNCgJMmBBx6Ym2++OVVV5Z577sl6662XzTbbrK3vyvpY25LWgdpnn33So0ePJMkuu+ySJ554IklSSsncuXMzf/78vPzyy+nZs+cia0YlyeTJk7P11ltnyy237JwCAACAFaJHVw8AYFUzduzYtLa2Zvbs2RkyZEjGjx+fW2+9NTNmzEgpJVtssUXbbJ39998/P/rRj7LTTjulV69eufjii9vO8/jjj2fmzJlpaWnpqlKW2fe///0cfvjhSZJ9990306ZNy+DBg/P3v/89X/7yl18TQq2sgRkAANAxgiSADpowYcJr2o4//vgl9i2l5IwzzmibodTeVlttlUceeWS5j29Fu+CCC9KjR48cddRRSZKHHnooa665Zn73u99lzpw5OfDAA7PXXntlwIABSZJ58+Zl0qRJOeuss7pw1AAAwPIgSAKoaYPLZy7jkb2S1o4fO+fE/st4vRXnqquuyk9/+tPccMMNKaUkSW655Za8613vylprrZU+ffpkt912y3333dcWJN16663ZYYcdsskmm3ThyAEAgOXBGkkA1HLbbbfl61//en74wx+mV69ebe2bbbZZpkyZkiSZO3dufv3rXy8yA2tlfaytI2+me+KJJ7LZZpu1vYHutNNOaztm3rx5OfXUU7Pzzjtn6NChueGGGzq9FgAA6CyCJABeY+zYsdl///0zffr0DBkyJFdeeWVOP/30vPjiixk1atQiYcqRRx6ZuXPnZtiwYdlnn31yzDHHZLvttkvSCJZuv/32HHrooV1ZzhJ15M10SbL11lu3vYWuffsFF1yQPn365N57783dd9+9Sq55BQAAdXm0DYDX6Mg6UL169coVV1yxxH3rrLNO/vjHPy7XsS0vI0eOzOOPP75I2z777NP2eZdddsmNN9641PNcddVV+dWvfpUkWWONNbLRRhst34ECAMBKRJAEQBJrQC2u/ZvpksZb9nbfffesu+66+cIXvpARI0a0Pfp27rnnprW1NVtvvXW++tWvWg8KAIBuy6NtALCYxd9Mt/HGG+e3v/1t7rzzznz5y1/OSSedlOeffz4LFizIzJkzs9tuu2XKlCkZOnRovvCFL3Tx6AEAYMURJAFAOwvfTHfppZe2vZmuZ8+e2XDDDZMkO+64YwYMGJBHH300G264YXr16tW2BtSoUaPywAMPdNnYAQBgRRMkAUDT672Z7tlnn82CBQuSJI899lj+8Ic/ZMCAASml5IADDsidd96ZJJk8eXIGDx7cJWMHAIDOYI0kAFZLY8eOTWtra2bPnp0hQ4Zk/PjxueiiizJv3ryMGjUqSTJ06NBcdNFFue+++/KJT3wiPXr0yBprrJELL7wwvXv3TpKcffbZOfnkk/PZz342G2+8cS6++OKuLAsAAFYoQRIAq6WOvJlun332ycknn7zEfVtuuWUmTZq0XMcGAAArK0ESAKsNb6YDAIB/jjWSAAAAAKhFkAQAAABALYIkAAAAAGoRJAEAAABQiyAJALqhcePGZeDAgRk+fHhb2/XXX59hw4ald+/eue+++9ra58+fn4985CMZMWJEdt1111x44YWLnGvBggXZfffd8773va/Txg8AwMpJkAQA3dCYMWNyzTXXLNK27bbb5nvf+15GjBixSPttt92WefPmZerUqbnjjjty+eWX5/HHH2/b/81vfjODBw/ulHEDALByEyQBQDc0cuTI9O7de5G2wYMHZ9CgQa/pW0rJ3LlzM3/+/Lz88svp2bNn1ltvvSTJzJkz87Of/SzHHXdcp4wbAICVmyAJAFZz++67b9ZZZ50MHjw42223XT7+8Y+3hVCf/exn82//9m9ZY42V7z8ZOvL43qRJk9LS0tK29e7dOw888ECS5P7778+IESOy00475TOf+Uyqqur0WgAAVhUr338VAgCd6qGHHsqaa66Z3/3ud/nNb36Tb3zjG3nsscdyyy23pE+fPtlxxx27eohL1JHH9w488MC0tramtbU13/rWt7LVVlvlHe94R5Lkk5/8ZL72ta9l2rRp+cMf/pDbbrut02oAAFjVCJIAYDV3yy23ZN99981aa62VPn36ZLfddst9992Xu+++O5MmTcr222+fsWPHZsqUKfnwhz/c1cNt05HH99q79tprM3r06CTJk08+mRdeeCFDhw5NKSXvf//7c/PNN6+wMQMArOoESQCwmttss80yZcqUJMncuXPz61//OoMGDcpZZ52Vhx9+OA8++GAmTJiQPfbYI9/+9re7eLT/vOuuu64tSJo1a1b69evXtq9fv36ZNWtWVw0NAGClJ0gCgG5o7Nix2X///TN9+vQMGTIkV155ZW666aYMGTIk99xzT4466qgcfvjhSZIjjzwyc+fOzbBhw7LPPvvkmGOOyXbbbdfFFawYv/71r9OrV68MGTKkq4cCALBK6tHVAwAAlr8JEyYssf3QQw99TVuvXr1yxRVXvOH5dt999+y+++7LZWxdqf1jbUnSt2/fPPHEE23fn3jiifTt27crhgYAsEoQJAFAN7HB5TOX8cheSWvHj51zYv9lvF7XePXVV3P99ddn0qRJbW2bbbZZ1l133dxzzz3ZZZddcvXVV69U60ABAKxsPNoGAKySOvL4XpL87//+b/r3758BAwYscp7/+I//yCc+8YnstNNO2XrrrbPffvt1ciUAAKsOM5IAgFXCa2Zctfxb0tL4+ESSTyxI8tckp9+aJHkmyS/ajuuV5K3JMVcsYebWJsmHr0mSXJrk0u/+36Nuq9qsKwCAFc2MJAAAAABqESQBAKykxo0bl4EDB2b48OFtbddff32GDRuW3r1757777mtr/9vf/pZDDjkk/fv3z+mnn77IeebNm5dTTz01O++8c4YOHZobbrih02oAALoXQRIAwEpqzJgxueaaaxZp23bbbfO9730vI0aMWKR97bXXzuc///mcc845rznPBRdckD59+uTee+/N3XffnZaWlhU6bgCg+xIkAQCspEaOHJnevXsv0jZ48OAMGjToNX3XWWedDB8+PGuvvfZr9l111VU57bTTkiRrrLFGNtpooxUz4A7qyIyrJLnwwguz0047ZZdddsnPf/7zJMn06dPT0tLStm2xxRa55JJLOrUOAFidCJIAALqxOXPmJEnOPffc7LHHHvnABz6Qp59+uotH1dCRGVe/+93vcu211+auu+7KNddck0996lNZsGBBBg0alNbW1rS2tmby5Ml585vfnEMOOaQzywCA1YogCQCgG1uwYEFmzpyZ3XbbLVOmTMnQoUPzhS98oauHlaRjM64mTpyY0aNHZ+21186AAQPy1re+Nffee+8ifSZPnpytt946W2655QodNwCszgRJAADd2IYbbphevXrl0EMPTZKMGjUqDzzwQBePquNmzZqV/v37t33v169fZs2atUifa6+9NqNHj+7soQHAakWQBADQjZVScsABB+TOO+9M0pi1M3jw4C4e1fI3b968TJo0KaNGjerqoQBAt9ajqwcAAMCSjR07Nq2trZk9e3aGDBmS8ePHp3fv3jnjjDPy17/+NUcddVS23377nHfeeUmS7bffPi+88EJeeeWV3HzzzbnuuuuyzTbb5Oyzz87JJ5+cz372s9l4441z8cUXd3FlHde3b9/MnDmz7fsTTzyRvn37tn2/9dZbs8MOO2STTTbpiuEBwGpDkAQAsJKaMGHCEtsXPqa20PTp05MkDz744BL7b7nllpk0adLyHVwnO/DAA/OhD30o48aNy6xZs/Loo49m5513btvvsTYA6ByCJACAlcQGl89ceqcl6pW0dvzYOSf2X3qnFagjM6623XbbvPe9781uu+2WHj165IILLsiaa66ZJJk7d25uv/32XHTRRV1aDwCsDgRJAAB0iY7OuPr0pz+dT3/606/pv8466+SPf/zj8h8gAPAagiQAADrF6jbjCgC6I29tAwCAFWTcuHEZOHBghg8f3tb27LPPZtSoUXnnO9+ZUaNGZc6cOUmSO++8M1tuuWVaWlrS0tLStoh60lhIfcSIEWlpaclee+3V2WUAQBtBEgAArCBjxozJNddcs0jbRRddlD333DPTpk3LnnvuucjaTsOHD09ra2taW1tzxhlnLHLcTTfdlNbW1txxxx2dMXQAWCJBEgAArCAjR45M7969F2mbOHFijj766CTJ0UcfnZtvvrkrhgYAy0SQBAAAnejpp5/OZpttliTZdNNN8/TTT7ft+9WvfpWRI0fmiCOOyCOPPNLWXkrJe9/73uy555757ne/29lDBoA2FtsGAIAuUkpJKSVJssMOO+TBBx/MW97ylvzsZz/LMccck2nTpiVJbrnllvTr1y/PPPNMRo0alUGDBmXkyJFdOXQAVlNmJAEAQCfaZJNN8uSTTyZJnnzyyfTp0ydJst566+Utb3lLkmT//ffPK6+8ktmzZydJ+vXrlyTp06dPDjnkkLaACQA6myAJAAA60YEHHpgf/vCHSZIf/vCHOeigg5IkTz31VKqqSpLce++9qaoqG264YebOnZsXXnghSTJ37tzcfvvt2Xbbbbtm8ACs9jzaBgAAK8jYsWPT2tqa2bNnZ8iQIRk/fnxOO+20nHDCCfne976XLbbYIt/97nfz17/+NTfccEMuu+yyrLnmmnnzm9+cCRMm5P9n797jLZvrx4+/3oOv+zByGXdhqMklQhxTE7lEuaSLWzFS+ppCFyQpt8pXvroQufsihPBziSQxkhFm3CMjl8olwiD3mXn//lhrH3uOM2PPzD5nnb3O6/l4nMfea+29zftjn7P3Wu/1eb8/EcEzzzzDrrvuCsDUqVP59Kc/zWabbVbxyCRJg5WJJEmSJKmPnH766b3uv/zyy6fb/ve//81ee+3FXnvt9bbnrrTSSvzpT3/qk/gkSZpVJpIkSZKkNln0zMdn85ULwE2z/trJeyw7m/+eJEmzxx5JkiRJkmbbV77yFVZddVU22mij7n3PP/8822+/Peuuuy7bb789L774IgCZyYEHHsg666xDV1cXd95553T/rRdffJGRI0dywAEH9OsYJEmtM5EkSZIkabbtsssu/PrXv55u309+8hNGjx7NxIkTGT16NGeddRYA1157LQ8//DATJ07kZz/7Gd/85jene90PfvADurq6+i12SdKsM5EkSZIkabZtvPHGDBs2bLp9V111FTvvvDMAO++8MzfccEP3/p122omIYP311+eFF17gqaeeAuDOO+/kmWeeYZNNNunX+CVJs8ZEkiRJkqS2evrppxk+fDgASy21FM899xwATz75JMsu+1Zfp2WWWYYnn3ySadOm8Z3vfIcjjzyyknglSa0zkSRJkiSpz0QEETHT55x22mlsscUW0yWZJEkD0zuu2hYRZwCfAJ7OzDXKfYsBFwArAY8Cn83M56P4hvgZsDXwCjAmMyeWr9kdOKT8z34/M89q71AkSZIkDQRLLrkkTz31FMOHD+epp57qLn1beumlefzxt1ane+KJJ1h66aW57bbbGD9+PKeddhovv/wyb775JgsuuCCHHXZYRSOQJM1IKzOS/g/4WI99BwHXZeYI4LpyG2ArYET5sxfwC+hOPB0KfBDYADg0IoYhSZIkqXa22morzj//fADOP/98Ro8e3b3/V7/6FZnJbbfdxtChQxk+fDinnnoq9957L/fccw9HHnkkO+20k0kkSRqg3jGRlJk3As/12L0d0JhRdBawfdP+s7NwC7BoRCwNbAlcm5nPZebzwLW8PTklSZIkqcPsueeebLHFFkyaNImRI0dy9tln8/Wvf53rr7+eddddlxtuuIHdd98dgC222IKVVlqJddZZh/32249jjz224uglSbPqHUvbZmCpzHyyvP8UsFR5f1ngH03P+2e5b0b7JUmSJHWIRc98/O07Rx0Bo4q7TwD7ToV9L3sVPvkLAB4Gxt0D3FO+9n1fL36ATe4E7uz53/wIjPwIp575OJP38JRBkgaa2U0kdcvMjIhsRzDNJk2aNIuvWKDdIczUrMc3p/p3fFD/MTq+vlD3MTq+dvJ3tC84vnar+xgdX1+o+xjrPr7Z10mxzq66j7Hu44P6j9HxtceIESNm+vjsJpL+FRFLZ+aTZena0+X+x4Hlm563XLnvceAjPfbfMLN/4J0Cf5uberk60odmOb451c/jg/qP0fH1gbqP0fG1lb+jfcDxtV3dx+j4+kDdx1j38c2mSZMmdUyss6vuY6z7+KD+Y3R8/aeVZtu9uRzYvby/O3BZ0/7dorAh8EJZAncNsEVEDCubbG9R7pMkSZIkSVKHeMcZSRFxPsVsosUj4p8Uq6/9D3BhROwJPAZ8tnz6VcDWwEPAK8AeAJn5XEQcCdxWPu+IzOzZwFuSJEmSJEkD2DsmkjJz5xk89NFenpvAV2bw3zkDOGOWopMkSZIkSdKAMbulbZIkSZIkSRpkTCRJkiRJkiSpJSaSJEmSJEmS1BITSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSpJk44YQT2HDDDdloo43Yc889ee211xg3bhwf/vCH2WijjTjssMOYMmUKAA8++CCbb745Sy65JMcff3zFkUtS+5lIkiRJkqQZeOKJJzj55JO5/vrrGT9+PFOnTuWiiy5i7NixnHHGGYwfP57hw4dz3nnnATBs2DCOPvpo9tlnn4ojl6S+YSJJkiRJkmZi6tSpvPbaa0yZMoVXX32VBRdckHnmmYdVV10VgA9+8INcccUVACyxxBKsu+66zD333FWGPEt6m3G11VZbMWrUKEaNGsV73vMe9t9//+7n//GPf2TUqFFsuOGGbL311hVGLqkKJpIkSZIkaQaWWWYZvvrVr7LGGmuw+uqrM3ToUD75yU8yZcoU7rjjDgCuu+46Hn/88YojnT29zbi6+OKLufrqq7npppu46aabWH/99dlkk00AmDx5Mvvvvz/nn38+t9xyC2eddVbFI5DU30wkSZIkSdIMTJ48mauuuoq77rqLBx54gJdffpkLL7yQM844g4MPPphNN92UBRdckCFDOvfUqueMq6WXXrr7sRdffJEbb7yR0aNHA/DrX/+abbbZhuWXXx4oZmBJGlw699NOkiRJkvrYDTfcwIorrsjiiy/OPPPMwzbbbMOtt97KBhtswNVXX80f/vAH1llnne4yt07T24yrTTfdtPvx3/zmN4wePZqFFloIgIceeojJkyfz8Y9/nNGjR3P++edXFbqkiphIkiRJkqQZWG655bj99tt55ZVXyEzGjRvHaqutxjPPPAPA66+/zllnncUee+xRcaSzp7cZVxdccEH34xdffDGf+tSnurenTp3KnXfeyYUXXsgll1zCMcccw0MPPVRF6JIqYiJJkiRJkmZgvfXWY9ttt2X06NF0dXUxbdo0xowZw3HHHccGG2zAxhtvzIc+9KHu0q9//etfjBw5khNPPJFjjjmGkSNH8uKLL1Y8ihmb0YwrgGeffZYJEyaw5ZZbdj9/mWWW6S7ne9e73kVXVxf33ntvVeG3rLeG4nvvvTdrrbUWo0aNYpddduHuu++e7jUTJ07kXe96F5dddllFUUsDU+csJSBJkiRJfWjRM2fQMHvp3WHs7gDcD1x43r9h5f+Gvf8bgJ8AP2l+7QHXdt99EVjh4peAl972n528x7LtCXwONM+4mn/++Rk3bhzrrLMOAJdddhlbbrkl8803X/fzt956aw444ACmTJnCG2+8wYQJExg7dmxV4bek0VD8z3/+M/PPPz9jxozh4osvBuDII49ku+22Y9KkSYwYMaL7NVOnTuXQQw+drsxPUsFEkiRJkiQNUs0zruaee27WXHNNxowZAxRlbV//+tene/7qq6/OZpttxsYbb8yQIUP4/Oc/z8iRIyuIfNY0GorPM888b2so3puTTz6ZbbfdlokTJ/ZThFLnsLRNkiRJkgaxgw8+mNtuu43x48dzyimnMO+88wJFo+3NNtvsbc/fd999+fOf/8z48eMH/GwkmHlD8SOPPJKuri5+/OMf8/rrrwPFDKYrr7ySPffcs8qwZ1lv5Xtf/epX2Xjjjenq6uJb3/oW//nPfwA499xzWWWVVRg1ahSjRo3i7LPPrjh6dRJnJEmSJEnSIDHD8r13tADcNOuvHQjle80NxRdZZBF23313LrjgAg499FCWWmop3njjDb7whS/w05/+lG9961t8+9vf5vDDD2fIkM6ZdzGj8r0f/vCHDB06FICxY8dy6qmnds8y22GHHTjmmGOqDHuWnHDCCZxzzjlEBCNHjuSEE05g//3354477iAzGT58OGeffTYLLbQQP//5zznnnHOYa665WHzxxfn5z3/OCiusUPUQaqNz/jIkSZIkSZpFM2ooPnz4cCKCeeedl2222aa7jO2OO+7gC1/4AmuuuSaXX3453/zmN7nyyisrHsU7a5TvTZkypbt8r5FEykxef/11IqLiKGdPI1F2/fXXM378eKZOndqdKPvTn/7EzTffzPDhwzn11FMBWGuttbj++uu5+eab2W677Tj00EMrHkG9mEiSJEmSJNVWc0PxzGTcuHGsttpqPPXUU0CRZLnhhht473vfC8Ddd9/NPffcj2H9TgAAIABJREFUwz333MO2227Lscceyyc+8Ykqh/COZla+N3bsWFZbbTUeffRR9tprr+7XXH755XR1dbHbbrvxz3/+s6rQWzYribIPf/jDLLDAAkDRB+yJJ56oLO46MpEkSZIkSaqt5obiXV1dTJs2jTFjxvClL32Jrq4uurq6mDx5Mvvvv3/Voc625vK9Bx54gJdffpkLLrgAgBNPPJEHHniAlVZaiUsuuQSArbbairvvvpubb76ZTTbZhL333rvK8N/R7CTKGn75y1/22utrIOqtz9Upp5zCOuusw/rrr8+zzz7b/dzJkyez66670tXVxaabbspf/vKXfovTHkmSJEmSpFqYYQ+opXeHsbsDcD9w4Xn/hh1O6n74fmC5i14AXpj+dRscwvnPATP47w6EHlAwffke0F2+t+OOOwIw11xzscUWW3DJJZfwuc99jsUWW6z7tbvtttuAL/2aUZ+rHXfckRNPPJGpU6ey1157dY+v4YILLuCOO+7gN7/5TYXRt2ZGfa4++MEPsuWWW/Kxj31suucfe+yxrLnmmpx77rk8+OCD7L///lx++eX9EqszkiRJkiRJ6mAzKt97+OGHgaL068Ybb2TEiBEA3WV9AFdddRWrrbZaJXG3akZ9rhoaibIrrrhiutcce+yxnH/++d0rEQ50vZXvrb322qy44opve+5f//pXPvzhDwOw2mqr8fe//52nn366X+J0RpIkSZIkSR2suXxv7rnnZs0112TMmDFsu+22vPTSS2QmK6ywAkcddRQAJ598MldffTVzzTUXw4YN48QTT6x4BDPXnCibf/75GTduHOussw4PP/wwK6+88tsSZXfddRdf+9rXuPjii1liiSUqjr41zeV78803H5tuuml3+V5v1lhjDa644gq6urqYMGEC//jHP3jiiSdYcskl+zxWE0mSJEmSJHW4gw8+mIMPPni6fddcc033/UmTJnU3pz700EMHfDlbs1lNlH3ve9/j5ZdfZvfdi3LG5ZZbjl/96ldVDuEdzax8rzdf+9rXOOiggxg1ahQjR45krbXWYsiQ/ik6M5EkSZIkSVIHmGEPqJYsADfN+uv7uw/ULPW52umM7ofvB1a4+CXgJdj+RNgenikfu3cm/91O6XPV09ChQ7tnkmUma621FiuttFK/xGqPJEmSJEmSpArNqM/VjEyePJk33ngDgLPPPpuurq7uGWd9zUSSJEmSJElShZrL97q6upg2bRpjxozhpJNOYuTIkTz99NNsvPHG7LPPPgA8+OCDbLTRRqy33npce+21HH300f0Wq6VtkiRJkiRJ/WCm5Ym9le/Nuw0csA0ATwHnAOec+TiwLHz1cgAeAq689GXg5bf9J/uidM8ZSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqiYkkSZIkSZIktcREkiRJkiRJklpiIkmSJEmSJEktMZEkSZIkSZKklphIkiRJkiRJUktMJEmSJEmSJKklJpIkSZIkSZLUEhNJkiRJkiRJaomJJEmSJEmSJLXERJIkSZIkSZJaYiJJkiRJkiRJLTGRJEmSJEmSpJaYSJIkSZIkSVJLTCRJkiRJkiSpJSaSJEmSJEmS1BITSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqiYkkSZIkSZIktcREkiRJkiRJklpiIkmSJEmSJEktMZEkSZIkSZKklphIkiRJkiRJUktMJEmSJEmSJKklJpIkSZIkSZLUEhNJkiRJkiRJaomJJEmSJEmSJLXERJIkSZIkSZJaYiJJkiRJkiRJLTGRJEmSJEmSpJaYSJIkSZIkSVJLTCRJkiRJkiSpJSaSJEmSJEmS1BITSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqyRwlkiLi0Yi4JyLujIjby32LRcS1ETGpvB1W7o+IOC4iHoqIuyNi3XYMQJIkSZIkSf2jHTOSNsnM92fmeuX2QcB1mTkCuK7cBtgKGFH+7AX8og3/tiRJkiRJkvpJX5S2bQecVd4/C9i+af/ZWbgFWDQilu6Df1+SJEmSJEl9YO45fH0Cv4uIBE7OzFOApTLzyfLxp4ClyvvLAv9oeu0/y31P0otJkybNYigLzOLz58ysxzen+nd8UP8xOr6+UPcxOr528ne0Lzi+dqv7GB1fX6j7GB1fO/k72hccX7vVfYyO7+1GjBgx08fnNJE0KjMfj4glgWsj4oHmBzMzyyTTLHunwN/mpsdn55+ZbbMc35zq5/FB/cfo+PpA3cfo+NrK39E+4Pjaru5jdHx9oO5jdHxt5e9oH3B8bVf3MTq+WTdHpW2Z+Xh5+zRwKbAB8K9GyVp5+3T59MeB5Ztevly5T5IkSZIkSR1gthNJEbFgRCzcuA9sAdwLXA7sXj5td+Cy8v7lwG7l6m0bAi80lcBJkiRJkiRpgJuT0ralgEsjovHfOS8zfxsRtwEXRsSewGPAZ8vnXwVsDTwEvALsMQf/tiRJkiRJkvrZbCeSMvNhYO1e9j8LfLSX/Ql8ZXb/PUmSJEmSJFVrjnokSZIkSZIkafAwkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqiYkkSZIkSZIktcREkiRJkiRJklpiIkmSJEmSJEktMZEkSZIkSZKklphIkiRJkiRJUktMJEmSJEmSJKklJpIkSZIkSZLUEhNJkiRJkiRJaomJJEmSJEmSJLXERJIkSZIkSZJaYiJJkiRJkiRJLTGRJEmSJEmSpJaYSJIkSZIkSVJLTCRJkiRJkiSpJSaSJEmSJEmS1BITSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqiYkkSZIkSZIktcREkiRJkiRJklpiIkmSJEmSJEktMZEkSZIkSZKklphIkiRJkiRJUktMJEmSJEmSJKklJpIkSZIkSZLUEhNJkiRJkiRJaomJJEmSJEmSJLXERJIkSZIkSZJaYiJJkiRJkiRJLTGRJEmSJEmSpJaYSJIkSZIkSVJLTCRJkiRJkiSpJSaSJEmSJEmS1BITSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqiYkkSZIkSZIktcREkiRJkiRJklpiIkmSJEmSJEktMZEkSZIkSZKklphIkiRJkiRJUktMJEmSJEmSJKklJpIkSZIkSZLUEhNJkiRJkiRJaomJJEmSJEmSJLXERJIkSZIkSZJaYiJJkiRJkiRJLTGRJEmSJEmSpJaYSJIkSZIkSVJLTCRJkiRJkiSpJSaSJEmSJEmS1BITSZIkSZIkSWqJiSRJkiRJkiS1xESSJEmSJEmSWmIiSZIkSZIkSS0xkSRJkiRJkqSWmEiSJEmSJElSS0wkSZIkSZIkqSUmkiRJkiRJktQSE0mSJEmSJElqiYkkSZIkSZIktcREkiRJkiRJklpiIkmSJEmSJEktMZEkSZIkSZKklphIkiRJkiRJUktMJEmSJEmSJKklJpIkSZIkSZLUEhNJkiRJkiRJaomJJEmSJEmSJLWk3xNJEfGxiPhrRDwUEQf1978vSZIkSZKk2dOviaSImAs4AdgKGAnsHBEj+zMGSZIkSZIkzZ7IzP77xyI2Ag7LzC3L7W8DZOZRAC+88EL/BSNJkiRJkqQZWmSRRaLnvv4ubVsW+EfT9j/LfZIkSZIkSRrgbLYtSZIkSZKklszdz//e48DyTdvLlfuA3qdMSZIkSZIkaWDo7xlJtwEjIuLdEfFfwE7A5f0cgyRJkiRJkmZDv85IyswpEfFV4BpgLuCMzLyvP2OQJEmSJEnS7OnXVdsGkohYEpivsZ2Zf68wnLaLiGHACKYf443VRSS9JSJWAf6Zma9HxEeAtYCzM3NytZFJqouIWBB4NTOnRcRqwHuAqzPzzYpDkwCIiI2BOzPz5Yj4HLAu8LPMfKzi0CTVQEQMAT6dmRdWHYvqZ9A1246IbSNiEvAIMA54FLi60qDaLCK+CNxIMfPr8PL2sCpjaqeI+ExELFzePyQiLomIdauOq13qPr7SxcDUiFgVOIWid9p51YY05yJi0/J2h95+qo6vXSLiRxExNCLmiYjrIuKZ8iSoViJi4zIZQUR8LiJ+HBErVh1XO0TEauV7d2+5vVZEHFJ1XG12IzBfRCwL/A74PPB/lUbURuV7eGpE/C4i/tD4qTqudoqIj0fEgRHxvcZP1TG12S+AVyJibeCbwN+As6sNac5FxE3l7UsR8WLTz0sR8WLV8bVTnb8noP7f93U/5s7MacCBVcfRFyLiwPL2+Ig4rudP1fG1S0QsERH/GxFXDbTv+kGXSAKOBDYEHszMdwMfBW6pNqS22w9YH3gsMzcB1gHqNNPju5n5UkSMAjYDTqc4GKuLuo8PYFpmTgE+CRyfmQcAS1ccUzuMLm+36eXnE1UF1Qe2yMwXKcb0KLAqcEClEfWNWp7klU4Fvg28CZCZd1P0LayTyMxXgB2AEzPzM8D7Ko6pnS4CJgKHUPz9NX5qISJOAnYE9gEC+AxQmxP00pQsSgO2A36emScAC1cc0xzLzFHl7cKZObTpZ+HMHFp1fG1W5+8JqP/3/WA45v59ROwfEctHxGKNn6qDaoP7y9vbgQm9/NTFuRRjfTfFBJFHKfpOV66/V20bCN7MzGcjYkhEDMnM6yPip1UH1WavZeZrEUFEzJuZD0TE6lUH1UZTy9uPA6dk5m8i4vtVBtRmdR8fwJsRsTOwO0WSBWCeCuNpi8w8tLzdo+pY+ljju+PjwEWZ+UJELRfdnJKZGRGNk7zTI2LPqoNqkwUy89Ye79uUqoLpIxERGwG7Ao33ba4K42m3KZlZtxOeZl2ZuVZE3J2Zh0fEsdRsBjnwUkR8G/gc8OGyDKXjvwubRcRcwFI0nXPUrJ1Enb8noP7f94PhmHvH8vYrTfsSWLmCWNomM68ob8+qOpY+9q7yc2W/zBwHjIsIE0kVmRwRC1FMeT83Ip4GXq44pnb7Z0QsCvw/4NqIeB6oU7394xFxMrA5cHREzEu9ZtfVfXwAewD/DfwgMx+JiHcD51QcU9uU79mngJWY/uD5iKpiarMrI+IB4FVg74hYAnit4pj6Qp1P8v4dRa+yBIiITwNPVhtS232NYtbVpZl5X0SsDFxfcUxzrOlK8hURMRa4FHi98XhmPldJYO33ann7SkQsAzxLPWauNtsR2AXYMzOfiogVgGMqjqltImIf4FDgX8C0cndS9EWsizp/T0D9v+9rf8xdVuDUVhQ9EPfn7cfcm1YVU5s1+jo+GREfB54ABsSMskHXbLusY36V4kNiV2AR4Jc1OvCaTkSMphhjbRqMRsQCwMeAezJzUkQsDayZmb+rOLS2qPv4BoOI+C3wAsXU2sbVLjLz2MqCarPyZPaFzJxafq4unJlPVR1XO0XEcIqTvNsy84/lSd5HMrPjyxbKpMopQBfwPEXfwM9l5qNVxtUXyotHZOZ/qo6lHSLiEYqT8d6mBWRmdvRV5oaI+C5wPEULghMoxnxaZn630sDUsoh4CPhgZj5bdSx9pc7fEw11/r4fDMfc5Ri/AayQmXtFxAhg9cy8suLQ2iIi7gJO4u3H3LUob4uITwB/pOgnezwwFDg8My+vNDAGZyLp6Mz81jvt62QRcTpF35k7m/YdlpmHVRfVnHunet46JAPLKeD3ZeZ7qo6lL0TEPZQzIHqTmbW4ShkR92bmGlXH0VfqflAC3X+Lvy/7zNVWeVIwJDNfqjqWdouINSl6lSxGkXR5BtgtM++rNDDNsnKWwHyZ+ULVsbRDRLzEzL8La9FHKCKuBzYveyKqAw2S7/tRwIjMPLOccbVQZj5SdVztEhEXUCRZdsvMNcr39ObMfH/FobVFREzIzA9UHcdgNBhL2zYHeiaNtuplXyfbElgvIn7cVDe6LZ2/ctsEZnIVlg6v9QUor/b8NSJWqFkPgYY6NZyemZsjYs3MvKfqQPrImRR/j13l9uMUjX9rc2BZ/i1Oi4hF6nLy2iwilgJ+CCyTmVtFxEhgo8w8veLQ2ulk4BuZeT1ARHyEosl418xe1Cki4jPAb8tGsYdQLB1/ZGbeUXFobRMRXTSVK0QEdZjpkZmNVaKOpCgpPYfi2GZX6lW+9zBwQ0T8hunLL39cXUjtMZNkYFDMDKxFMpCaf99HxKHAesDqFGOdB/glsHGVcbXZKpm5Y9mblMx8JerV6KrWZd5l6d4vgKXKROBawLaZWXkvr0GTSIqIvYGxwCoRcXfTQwsDf6omqj7zNLAJ8MuI2IBiFbeO/8Coe41vk2HAfRFxK039uzJz2+pCao/MrFOvrpkZBYwpS1Be560Dy1rMuKL+ByUN/wHuiYhrmf5vcd/qQmqb/6M4aP5Ouf0gcAHFijV1sWAjiQSQmTeUM7Dq4ruZeVG8tdrQMRTT+z9YbVjtERHnAKsAd/JWuUJSrxWxts3MtZu2f1GWaXyvqoDa7O/lz3+VP7XRSAYOAnX/vv8kxerWEwEy84mIqNt7+0ZEzM9bPRFXoSnhUgO7l7fNqwnWYoJB6VSKsZ0MxSq7EXEeYCKpH51HsdrHUcBBTftfqkvGskmUV9C3iYjDgBso+iR1tIh4T7kC3bq9PZ6ZE/s7pj5S2/4PEXFTZo7q5Upe3a7gbVV1AH2s7gclDZeUP3W0eGZeWDaJJTOnRMTUd3pRh3m47LPTaOT/OYoZEnVR99WG1gNGZr17MLwcEbsCv6L4PN2ZGi0Ak5mHVx1DX4mIoZn54ozaLtTo3KLu3/dvlKvuNcZXp4sNDYcCvwWWj4hzKWZbjak0ojYaBBMNBuwqu4MmkVQmVl4op38/lZmvl9Pc14qIszNzcrURtlV3863MPCwiJgBfrzCedvkGsBfQW8PiBGrRnT8zx0XEihT12r8va5lrsWR1Zo4qb+t2tWc6jZlXEbEkMF/F4fSFWh+UNGTmWeUB9AqZ+deq42mzlyPiXbx1crAhRYP4OvkCcDhvJQP/WO6ri7qvNnQvMJz6rSbYbBfgZ+VPUsyQ36XSiNqo7DdzIPA+mr4La7Ka0nkU5fq9tV2o02yIun/fX1h+ji4aEV+i+I44teKY2iozr42IicCGFL+n+2XmvysOq60iYg1gJNN/znT07NWmNicDdpXdwdhs+06Kq1wrAVcBlwHvy8ytq4yrncorsP+Xmf9o2rdXZp5SYVhqUflFthewWGauUjY2PCkzP1pxaG3XM9FSl75QEbEtRcJzGYpS0xWB+zPzfZUG1kZlEqJxUHJL3Q5KACJiG+B/gf/KzHdHxPuBI+pQZlrO7DweWIPihH0J4NOZefdMX6gBo66rDUXEFRQHzAsD7wduZfq+Fx3/9zdYRMTvKEpm9wf+m6IE5Zk6LXAzGNT9+z4iNge2oBjfNZl5bcUhtVVZirgrsHJmHhHFyoLDM/PWikNri7LP1UcoEklXUVQF3JSZn64yrjkVERMzc90yiXQyA3CV3cGYSGq8KQcCr2bm8RFxR2auU3Vs7RIRT1OsTvPVpiajEzOz15KwTlTHzHNDmezcAPhz4/cyIu7JzDWrjax96p5oKXtcbEqx6tc6EbEJxYf+nhWH1jYRsSzF+9Y9szUzb6wuovYrZ3NuCtzQ9LfY0SvyRcRnyr467wb+QdFgNIC/Zuab1UbXXmWDyv1patYMnT8bou4lNRExemaPZ+a4/oqlr0XEfMCevH3GTi1mzkW5mlJE3N3oERgRt2Xm+lXH1k4RMQwYwfTvYUd/Hw6idhK1FxG/AKYBm2bme8vf19/V5e8wihWh1wbuyMy1o1hM5JeZuXnFoc2RnvmJGICr7A6a0rYmb5YN43YDtin3zVNhPH3hcWA74KKI+HVmHkMNmm03zCjzTH0acL6emW80amEjYm5mskxwhzqS4urWdImWimNqpzcz89mIGBIRQzLz+oj4adVBtUtEHA3sCNxHcXACxe9oRx849+LNzHyhR136tBk9uUN8m2LFnYvLiwv3VRxPX7qIovn0abzVT6gOal1S00gURcTRPWeulJ89tUkkUfTveoBitd0jKGYN3F9pRO3VSE4/GREfB54Aek2AdqqI+CLFojbLUTSG3xAYT+e3W6h1O4leenV2P0S9enYCfLCcRHEHQGY+HxF1an7/amZOi4gpETGU4gL18lUH1QbLRsRxPXc2jkkHwsIvgzGRtAfF9NofZOYj5VXZc97hNR0nM/9eXtX7RURcBMxfdUxt9Gneyjzv0cg8VxxTO42LiIOB+cvptmOBKyqOqd1qnWgBJkfEQhSJlXPLWYK1aaAKbA+snpl1arjZm/siYhdgrrLEdF/g5opjmlPPleUmK0fE5T0frFnZ0JTM/EXVQfSBH8CgaDC6OdCzBGqrXvZ1slUz8zMRsV3Zk+08il5edfH9iFgE+CZFKe1Q6tGzs9l+wPoUJV+bRMR7gB9WHNMcy8y9ImIIcEhm1m1169r36uzhzYiYi7d67CxB518Ua3Z7RCxK0dtqAsWKu+OrDaktXqUYz4A16BJJmfkXipOBxvYjwNHVRdQnbgfIzNeAPSLiK8AHqg2preqaeW44iGKq+z3AlylmXZ1WaUTtV/dEy3bAaxQHzLtSrJp4RKURtdfDFDM5655I2gf4DsU4zweuoZhN18m2BtaluIDS25XmjtdU8nVFRIwFLmX6HjsdXfoFnEDxHhIR4zNzo4rjaauI2JviAsrKEdHcs2thOj+R21Njxs7ksmT/KWDJCuNpt/GNxW6ATaoOpo+8lpmvRQQRMW9ZDrZ61UG1Q3ms/XOgNu0/GmZUGtxQg++JZsdRfA8uGRE/oLggf0i1IbVPZo4t754UEb8Fhtak3+OzmXlW1UHMzGDskTQCOIq399fp6Kngg0lEnAgcDOxEcZXrP8CdmblHpYGpZWWd76sUKww1Ei2/rMsXd0TsQzGe56uOpZ0i4niKK1rLUswKvI7pT9Arn2armYuIczLz8xFxYGb+qOp4+kJEPMLbS74astO/75v7JtStxyNAOYNlGMWx2kFND71Ul++IhrIs6mJgLeBMYCHge5l5UqWBtUlETKIo9zoTuDpreNIREZdSVDt8jaLc63lgnros4hMR/0sxu+OSOr1/Pb4nVqB43wJYFPh73WZ8ljPlPkoxxusyszYltBFxMXAGxWdMbWZaRcQtmblh1XHMzGBMJN1EsZTlTyh6JO1B0bjqe5UG1gYRcWFmfrZsOva2N7bR6LBOImIl6pN5BrqbxvV8/16gmGn2/cx8tv+jaq8Z9b6oy0ouEfF9ikTnRIovt2vqcAAWEbvP7PGBfuVkVsVbq0c1a/wtnlzO+uwoEfEXYDPgaopec9MlW+p2ol5HZTP/j1Ak4v9Aj/exbu9h1HR1z8EgimYem1Esqb4+cCHFqsIPVhpYHylbSixCcUJbi8ULyl5CCwJTKGZa16qHUEScClyamVeV21sB22fml6uNrP2iWOlzJPBYZj5TdTztEhGbUZzPb0jRG/HMzPxrtVENDoMxkdRYQaJ7FazGvqpjm1MRsXRmPhkRK/b2eGY+1t8x9YWIuC4zP/pO+zpVRPyIojHseeWunYAFKKa8j8rMbWb02k4Rvawi2LyqSx2UB9BbUHy5rUdxAH16Zv6t0sDaoJxR9lpmTi235wLmzcxXqo2svSLiZ8ASFGVtUDQYf5EiuTQ0Mz9fVWyzKyL2BfamaMj8OD0aNXf6bJ1mZVn3uZk5udweBuycmSdWG9mciYhHKfpb1HLGVUNEbAP8mBqu7hkR35jZ45n54/6Kpb+Ui2r8kiIpcRdwUGZ2fB+TxizPd9rXaSJiw8y8peo4+lr0sipyb/s6URQrJB8HPEdRynYC8C+KlUy/VcOLf4sAO1O0JPgHRc+kX9YlqTsQDboeScDrZfO4SRHxVYoD6YUqjqldppYNi1el6K9zVGa+WHFMbRPFMrkLAIuXJwSNg+ihFKU2dbFZjyTLPY3ES0R09MpmTb0vVql774vMzIh4iiIBOIWiVOPXEXFtZh5YbXRz7DqKq8z/KbfnB34HdFUWUd/oyumXx70iyqWrI6IjVzvLzOOA4yLiF5m5d9Xx9LEvZeYJjY1ypZovAR2dSMrMlaqOoZ98n/qu7jkoGv1GxLso3rPPU5zA7gNcDryfYuZAHcqHpktslhdWOv7iNMXnZG17sTV5IiIO4a1Fe3alWF2wDo6kuKC5CHA9sFZmPlzO8rwOqE0iqcdnzR3AucAoYHeKWbvqA4MxkbQfRTJiX4o/sE0ofsnq4GyK7u7HUywNfBwwpsqA2uzLFDXoy1CMs5FIehH4eVVB9YG5ImKDzLwVICLWB+YqH5tSXVhtcR5FSU2te19ExH7AbsC/KRqlH5CZbzaS2ECnJ5Lmy8xGEonM/E85ZbpuFoqIFRqlNBGxAm9deHijurDmXGbuHRFrAx8qd91YpxLh0lwREY2y0vIEr05LHtddbVf3zMzDq46hn4ynaOy/fWb+s2n/7RHR0X2gIuLbFP0654+IxkXboPhuOLWywNqnecbjfDN8VufbmaLlyaXl9o3lvjqY1igjjYhHMvNhgMx8OiI6/XyiW9mnbHWKz5ptMvPJ8qELIuL26iKrv0HVtL+6AAAVRklEQVSTSCqzrwcz/WydujVnXjozv1PevyYiJlYaTZtl5s+An0XEvuVV9W4RMW9FYfWFPYEzo1jVDOAlYM+ynOio6sKac43VWyLi9z1LLSPifzLzoBm8tNMMA3boOcZyBZRPVBRTO70cEetm5kSAiPgARfP0uvkGcFNE/I3ioPrdwNjyb7Gjr+SVJW57AZeUu86NiFMy8/gKw2q331IcSJ5cbn+53KfOUPfVPRszrfekmNXS3AfqC5UF1SZl4vaKzOx1pcvM7OgVkzPzKOCoiDgqM79ddTx9YEg5+39I0/3a9WIrx7FfRCxcbL51kawGmt/DaT3ewyHVhdU+5QXaCZn5yd4ez8z1+jmkQWXQ9EiKYjnACRQHJJ8AFs7MMZUG1WZNDTgbHxLXN2/X5UM/Is5oPsgqT+our0OPpPLAa9/M/ElZ69tIvtRKRFxF0bvk3HL7BIpZLntWG9mcK9/D+zLzPVXH0lfKWXK/opj+HcBwYMfMnFBpYG1UHpx8GrgMaLyXf+3EBtu9KUtLN8rMl8vtBSmW6q5Tn7IhFMmjxnfDtcBpjd5eGtii99U9z63DghMNEXER8ACwC3AExTjvz8z9Kg2sTWpeEgVARByRTQv2lJ8752TmrhWGNccGUS+2NSkqOhYrd/0b2D0z760uqvYYRO9h7VYv7RSDKZF0V2au3bT9tma/nW4QfWAcASyemWPL7PpvgFMz88yKQ2uLiLg1MzeoOo6+FBHzU/RJOAP4GDC5LgfOABFxGbBP1nh1oYiYh2IqMRQJlto1M4yI2+t6NSuK1SHXbyTGypkRt9Whwag6X5mQ/31mblJ1LH2pcQLUWGyi/Fz9Yw7wJZ9bFRG/oOhheRFNs8ky85IZvqjDRMSZwIOZeVQ5O/5C4I7MPKzayNSKiLgZ+E5mXl9ufwT4YWbWredjbUXE/1KU0V6SgyWxMUAMmtI26F6xpZFkmat5uw6zdQZLA87M/F5E/Kisr/8A8D+ZeXHVcbXRnyLi58AFTH/g1fGlihGxWNPmF4H/B/wJODwiFqvD32FpGHBfRNzK9O/httWFNOciYtPM/ENE7NDjodUiolYnB6XfR8T+vP1vsQ6/p2cCfy57CwBsD5xeYTxtFxEjKMqBRzJ92VAtLqrUWWZOjYhpEbFIHWflNmkk4CdHxBoUizMsWWE87TYf8CywadO+5K2S2jr4AkXp5bcp+q5elZm16OU1SCzYSCIBZOYN5WxIdY4vU7QimBIRr1Gc22dmDq02rPobTDOSHmUQzNapsx4nrwF8F7iVsudFXU5iI+L6XnZnZm7ay/6OEhGPUBxERtNtQ23+DiNidG/7M3Ncf8fSThFxeGYeWl6B7Snr0NejWfn72lOdfk/XpVjVBIpZEHdUGU+7RcRNFE1UfwJsA+wBDGkuQ9HAVc7sXIeiJLE5kbtvZUG1WUR8EbgYWIsiubsQ8N3MPHmmL1Tlys/PhnmAkykujJ0O9bj4NxiUF1MmUjRqhmLlrw/MqOeOpLcMmkSSOt8MTl4bancSK0ntVva3Wjwzr+6xf2vgXzXrczUhMz8QEfc0SvYa+6qOTe8sInpdUTczO7rR/WAQEQdm5o8i4niKi0bTqUMycAYX/RpqcfFvMCirUw6n6aIKcFhmPl9dVGpFRLwnMx/okdTtZjK37w2q0jZ1thqusteriOj1anlmHtHfsfSViPgKRdPUyeX2MGDnzDyx2sjmTETclJmjIuIlpj94rtU023IVs1soDrj+mJn3VRxSn4iI3Xrbn5ln93csbXQ0xcycnu6jmBFRp5Of18vGt5Mi4qvA4xQzPtQB6pwwiohtgLuzXNmz/N7/FPAYsF9m9jYbspPcX97WduntuvfvGizKhNG+NV21re6+QbH67LG9PJbU63hmQHJGkjpORJxFcaDVnIQ4ti4zkiLim02b81GsMnh/XcYHEBF3Zub7e+xz1YUOUTYU/SDwIWBjiqbbd9dtKnh5Nb1hPorVvyZm5qcrCmmORcRtmbn+DB67u2artq1PcUK7KHAkxapfP8rMWyoNTC2pc4+rctXEDTPzlYj4BPBjYGeKUr7PZOaWlQaolkXEDyk+V5qPSb+ZmYdUG5laUedV26S+5owkdaK1Gl/YUFxNiIjaJCAyc7rMerkawTUVhdNX5oqIaKyuUK7Q818Vx9R2EbEAxUnQo5n576rjaaOpFE1ip1L0nnu6/KmVzNyneTsiFgV+VVE47TJsJo8t0G9R9IPMvK28+x96n4Wlge1M3upxtQllj6tKI2qfzMxXyvs7AKeXZaUTImJshXG1RUQsDnwFeJ5iddZjKC48/I0iyfJQheG121aZeXBjozwm3RowkdQZTga+0WPVtlMAV23rABGxIvByZv47IjakKFF8KDP/X8WhDQomktSJhkTEsEb9crkSWJ1/lxcAlqs6iDb7LXBBRDQain653NfRImJb4DjgOYqDyBOAfwErRcS3alSq8SJwD8VV9FMz89mK4+kvLwPvrjqIOfT7iPgBcEhTIjcoekT8odLI2qTspzej6daZmXv2ZzyabfNn5nXlRYfHgMMiYgJQh2bpERELAa9QzHRsLuuer/eXdJTzKMraRlAsinIm8DOKZNJpwEcqi6z95oqIeTPzdYCImB+Yt+KY1DpXbetQZUnw7kBGxK+AzYAbgI9HxEcy82tVxjcY1PnkW/V1LDA+Ii6i6D3zaeAH1YbUPhFxD2+dBM0FLAHUpj9S6VsUyaO9y+1rKQ4uO92RwBYUJTTXU8yeezgilgSuA+qSSNqZ4qrPWOCLEXEzcGNmXldtWO0VEVfw1t/iEIrZZRdWF1FbfJPib+2hiLiz3Lc2xUnfFyuLqr2u7GXf8sDXKT5T1Rnq3OPqp8CdFEn5+zPzdoBydvWTVQbWJktl5sFlkvqxzDym3P9A2SOxTs4FrmtaEGYP6vNdPxg8HBHfZfpV2x6uMB61bifgvRQX3P8ODC/Lheem+HxVH7NHkjpSRIykaKKWwPWZ+ZeKQ2qbcppmwxSKlZSmVBVPX4mI/6LorZPAXzPzzYpDmmPNfZ6aV4rq+VhdRMR7gK2ArwFLZub8FYfUVhExumlzCsUJ0T+riqedImJl4H3l5n2ZWcsD53KcBwMfpiiROj0z36g2KrWilx5XQ4Fj6tLjKiKWBZYE7srMaeW+pYF5MvPvlQY3hyJiYmau2/N+b9t1EBEfo5gNAXBtZtatHUFtuWpb5+rxOTPdMXYdP2cGImckqVPNQzEbqXG/NppWcWn013kDeKbSoNqsrEE/C3iU4n1cPiJ2z8wbq4yrDYaUByVDgGnl/cbvaV16exARF1PMYvkbcCOwG/DnSoPqA5k5rnG/7PlRmxK+MnFUy+QRdCc5D6FoXnwM8N91TMjXUTmD82BgVYoS2qPquGprZj5OMcuqeV8dZiMBrBwRl1N8/zXuU253enlwb+6gOBbN8r46RGPVtqrj0GxZNCJ2oPhcGVrep9xepLqwBg9nJKnjRMR+wJeAiyk+LD4JnJKZx8/0hQPczPrrAHXqr0PZ52KXzPxrub0acH5mfqDayOZMRDxK0Xw6enk467DaEEBErAfckZlTq46lL5QNG/+H4m/xSIop74tTJAN3y8yO7+dVZ2XZ8wcoyqAvpGgK3y0zn6siLrUmIn4LTKBIUn8CWDgzx1QalGZJj9mcb9OcpO90EfFZimT1DRTf/R8CDsjMX1cZl2bOXnqdr6mctFd1vAAx0JhIUscpl83dKDNfLrcXBMZ3+rLVEXEX8Blm0F+nuUyq0/W2zHjdlh6vo6arPb3KzEv6K5a+FBG3U8yIWIRi9ZatMvOWcpbL+XUrUaybMqHbOLhp3DaSu7VJ6NZVRNyVmWs3bVuioAGrPHbbPDOfLreXAH7f/DusgSciPtXL7u5eeplZt0VupLaztE2dKJj+CvNUep8B0mmmZeaDABHxSKNnSWY+HRF1K8m4PSJOA35Zbu9K0exXA9s25e2SFEvjNlb52gS4GahFIgmYOzN/BxARRzR6smTmA0XvWA1kmblS1TFozvQoC56redsZZRpghjSSSKVnqVEpe11l5sWN+z166f0PcHpVcUmdxESSOtGZwJ8j4tJye3vq8aE/KPrrlPYGvsJbdel/ZPrljzUANaYJR8S1wMhGP4+yQez/VRhau01ruv9qj8ecxiv1rUUoStuas7YTy9sEnFGmgeS3EXENcH65vSNwVYXxqEX20pPmjKVt6kgRsS5NKyxkZsc3Nxws/XXU+SLi/sx8b9P2EIqVv947k5d1jIiYCrxM8bc4P/BK4yFgvsysVYN/SdKsiYhVgaUy809l2XfjmHQycG5m/q266PRO7KUnzTkTSeoY5VLAi2fm1T32bw38KzMnVBOZWlX2t5oheyR1hoj4OTCC6a/APpSZ+1QXlSRpICkX0jgAWJGmKojM3LSyoNokIq4Evp2Z9/TYvybww8zcpvdXaiCwl169REQXxeJEzZ8zZ1cW0CBhIkkdIyL+AOyRmY/12L8icGYdDkzqLiLupPjCPg+4gh5lQz3fWw1c5RXYD5WbN2bmpTN7viRpcCkbUZ9EUarYPeOjDhf+IuK2zFx/Bo/dU6cFUqSBLCLOAVYB7uStz5nMzH1n/Cq1g4kkdYx3+NJ2xa8OUdak70zRuPkvFEml31mXLklSfUTEhMz8QNVx9IWImJSZI2bw2EOZuWp/xyQNRhFxP0XfTpMa/axuDXxVb8Nm8tgC/RaF5khmPpCZh5bLOV8BnE2x3Ko6RETsEBGTIuKFiHgxIl6KiBerjkuSNKBcERFjI2LpiFis8VN1UG1ye0R8qefOiPgixQwsSf3jXmB41UEMRs5IUseIiJMollU9pJF1jmIt7sOB4Zm5V5XxqTURsSywE/BJ4HmKJoeXZuZ/Kg1MLYuIh4BtMvP+qmORJA1MEfFIL7tr0X8mIpYCLgXe4K3E0XrAfwGfzMynqopNGkwi4nrg/cCtwOuN/Zm5bWVBDRImktQxImJB4DRgA4o6WIC1gduBL5qIGPgiYhywMEXy6GKKxGA3V8noDBHxp8zcuOo4JEmqUkRsAqxRbt6XmX+oMh5psImI0b3tz8xx/R3LYGMiSR0nIlYG3ldu3peZD1cZj1o3g1UyoFgpoxZXKQeDiPgZxTTi/8f0V38uqSwoSdKAEhELAN8AVsjMvSJiBLB6Zl5ZcWiSaqScIdjoo3trZj5dZTyDhYkkSdIsiYgze9mdmfmFfg9GkjQgRcQFFGVfu2XmGmVi6ebMfH/FoUmqiYj4LHAMcAPFhekPAQdk5q+rjGswMJEkSZIkqa0i4vbMXC8i7sjMdcp9d2Xm2lXHJqkeIuIuYPPGLKSIWAL4vZ8zfW/uqgOQJHWWiJgP2JOixHS+xn5nJEmSmrwREfNTlrJHxCo0lUNLUhsM6VHK9iyuTN8v/J8sSZpV51D0SNoSGAcsB7xUaUSSpIHmUOC3wPIRcS5wHXBgtSFJqpnfRsQ1ETEmIsYAvwGurjimQcHSNknSLGmUKUTE3Zm5VkTMA/wxMzesOjZJ0sAQEYtR9CzZsLy9BVg4Mx+pNDBJtRIROwCjys0/ZualVcYzWFjaJkmaVW+Wt5MjYg3gKWDJCuORJA08VwBbZeZvACLivcBFwBqVRiWpNiLi6Mz8FnBJL/vUhyxtkyTNqlMiYhjwXeBy4C/A0dWGJEkaYH4IXBERC0bEB4BfA5+rOCZJ9bJ5L/u26vcoBiFL2yRJkiS1XURsT9EXaWHgU5n5YMUhSaqBiNgbGAusAjzU9NDCwM2ZuWslgQ0iJpIkSS2JiG2AuzPzsXL7e8CngMeA/ex7IUmKiOMpV2orfRT4G/AoQGbuW0FYkmokIhYBhgFHAQc1PfRSZj5XTVSDiz2SJEmt+gFF01Qi4hMUJQo7A+sAJ1Gs4iZJGtxu77E9oZIoJNVWZr4AvBARUxoXOBsi4pzM/HxFoQ0aJpIkSa3KzHylvL8DcHpmTvj/7d2761xVFAXgtX0lJvkJagiCikEQwWCMBlMqpNLCQi2ilQSs/A9sBRGxEcROFBsRCyVYiJAEFLQy4gMRmxALn6DiCxUf22JGHCSGgVxz4sz3wTBz7mlWNcXi7HOTHKuqBwbmAuAs0d3PJklVbU3yc3f/Pl+fm2TTyGzAytm1uKiq85LsHZRlrbhsG4BlVVVtq6pzMhtVOLKwt3lQJgDOTkeSXLiwvjDJ4UFZgBVSVQ9W1fdJdlfVd1X1/Xz9RZJDg+OtBUUSAMt6PMk7mY0tfNjdbyVJVd2Y5LORwQA462zu7h/+Wsx/bxmYB1gR3f1Id28keay7L+rujfnn0u5+cHS+dWC0DYCldPfTVfVqkh1J3l3Y+jzJwTGpADhL/VhVN3X320lSVXuT/DQ4E7BaXqmqW/75sLtfHxFmnXhrGwAAMKmqujnJ80k+TVJJLktyYH63HsBpq6qXF5abk+xLcqy79w+KtDYUSQAAwOSq6vwk186XH3X3ryPzAKutqq5M8nh33z06y6pTJAEAAJOoqv3dfbSq7jrZfne/eKYzAeuhqirJB9193egsq84dSQAAwFRuTXI0yR0n2eskiiRgElX1RGb/K8nsRWJ7krw9LtH6cCIJAAAA+F+pqvsWlr8lOdHdb4zKs04USQAAwKSqalOSu5PszMIURHc/NCoTANMw2gYAAEztUJJvkxxL8svgLMAKqar3/m0rSXf37jOZZx0pkgAAgKld0d23jQ4BrKQ/Mrsb6bkkLyf5aWyc9XPO6AAAAMDKebOqrh8dAlg93b0nyb1JtmVWJj2cZFeST7r745HZ1oU7kgAAgElU1fuZnRQ4L8k1SY5nNtpm5AT4T1TVgSRPJnm0ux8bnWcdKJIAAIBJVNVVp9p3WgCYQlVdnuSeJHcm+SbJC0le6u4fhgZbE+5IAgAAprIjyfbufmXxYVXdnuTLJIok4LRU1WtJNjIrjw4m+Wq+dUFVXdLdXw8LtyacSAIAACZRVUeTHPznyaP5SaVnunv/mGTAqqiqE5mN0GbhO/l7hPbqMx5qzTiRBAAATGXjZONr3f1xVW0fEQhYLd29c3SGdeetbQAAwFQuPsXeljOWAoD/jCIJAACYyuGqeriq6q8HNfNQkqMDcwEwEXckAQAAk6iqrUmeSrIvyTvzxzckeSvJ/d6oBPD/p0gCAAAmVVVXJ9k1X37Q3cdH5gFgOookAAAAAJbijiQAAAAAlqJIAgAAAGApiiQAAAAAlqJIAgAAAGApiiQAAAAAlqJIAgAAAGApiiQAAAAAlqJIAgAAAGApfwKGW+byTPxjfQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhyDpY0Xa_q9"
      },
      "source": [
        "We can infer from the analysis that the 5 most liked dishes are **Pasta**,**Pizza**,**Cocktails**,**Burgers**,and **Mocktails**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL-g9fNIZ-jx"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-m_6KrvawN0"
      },
      "source": [
        "**Restaurant and their counts**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtnKfb9faw7k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "41868af0-aac6-44cf-9472-91a60a62cb54"
      },
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "rest=df['rest_type'].value_counts()[:20]\n",
        "sns.barplot(rest,rest.index)\n",
        "plt.title(\"Restaurant types\")\n",
        "plt.xlabel(\"count\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvmd2eXobbTG"
      },
      "source": [
        "**Casual Dining**, **Quick Bites** and **Cafe** are the 3 most common types of Restaurants in Banglore"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aa-QOQJCoOn1"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQeN9B0OsI86"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## **Most famous Restaurants**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDfmtqYmsBLC"
      },
      "source": [
        "# plt.figure(figsize=(15,7))\n",
        "# chains=df['name'].value_counts()[:20]\n",
        "# sns.barplot(x=chains,y=chains.index,palette='Set1')\n",
        "# plt.title(\"Most famous restaurant chains in Bangaluru\",size=20,pad=20)\n",
        "# plt.xlabel(\"Number of outlets\",size=15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "psfFRH6ihoFr"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vbke_FH9hs48"
      },
      "source": [
        "# **Building Our Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "js-tSAfOh12Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "ac5ebe79-7050-45c2-8977-6c5f5d1e75d0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>address</th>\n",
              "      <th>name</th>\n",
              "      <th>online_order</th>\n",
              "      <th>book_table</th>\n",
              "      <th>rate</th>\n",
              "      <th>votes</th>\n",
              "      <th>location</th>\n",
              "      <th>rest_type</th>\n",
              "      <th>dish_liked</th>\n",
              "      <th>cuisines</th>\n",
              "      <th>cost</th>\n",
              "      <th>reviews_list</th>\n",
              "      <th>menu_item</th>\n",
              "      <th>type</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.1</td>\n",
              "      <td>775</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
              "      <td>North Indian, Mughlai, Chinese</td>\n",
              "      <td>800.0</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
              "      <td>Spice Elephant</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4.1</td>\n",
              "      <td>787</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
              "      <td>Chinese, North Indian, Thai</td>\n",
              "      <td>800.0</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
              "      <td>San Churro Cafe</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>3.8</td>\n",
              "      <td>918</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Cafe, Casual Dining</td>\n",
              "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
              "      <td>Cafe, Mexican, Italian</td>\n",
              "      <td>800.0</td>\n",
              "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
              "      <td>Addhuri Udupi Bhojana</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3.7</td>\n",
              "      <td>88</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Quick Bites</td>\n",
              "      <td>Masala Dosa</td>\n",
              "      <td>South Indian, North Indian</td>\n",
              "      <td>300.0</td>\n",
              "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
              "      <td>Grand Village</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>3.8</td>\n",
              "      <td>166</td>\n",
              "      <td>Basavanagudi</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Panipuri, Gol Gappe</td>\n",
              "      <td>North Indian, Rajasthani</td>\n",
              "      <td>600.0</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             address  ...          city\n",
              "0  942, 21st Main Road, 2nd Stage, Banashankari, ...  ...  Banashankari\n",
              "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...  ...  Banashankari\n",
              "2  1112, Next to KIMS Medical College, 17th Cross...  ...  Banashankari\n",
              "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  ...  Banashankari\n",
              "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...  ...  Banashankari\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaQkfi6WsEcX"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rS7W-Gy-Dyc7"
      },
      "source": [
        "## **Convert the online categorical variables into a numeric format**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VW8c_k09mTvV"
      },
      "source": [
        "df.online_order[df.online_order == 'Yes'] = 1 \n",
        "df.online_order[df.online_order == 'No'] = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcfGQuLYmZHg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "68bee3f1-8b42-42b2-a2c5-66dd4a5a8445"
      },
      "source": [
        "df.online_order.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    16378\n",
              "0     6870\n",
              "Name: online_order, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATELEaVpsTeM"
      },
      "source": [
        "df.online_order = pd.to_numeric(df.online_order)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2zp3vVTOMrM"
      },
      "source": [
        "\n",
        "**change the string categorical into to a categorical int**\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sNVEq1Ntl_c"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1Nrkx23toBe"
      },
      "source": [
        "df.book_table[df.book_table == 'Yes'] = 1 \n",
        "df.book_table[df.book_table == 'No'] = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Me29fG_Ytkd6"
      },
      "source": [
        "df.book_table = pd.to_numeric(df.book_table)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8W23xdOtxmq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b78cdab8-ab69-47f2-ab57-7b285e70c871"
      },
      "source": [
        "df.book_table.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    17191\n",
              "1     6057\n",
              "Name: book_table, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OipBL6_2uuip"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yujdjvRPBn1"
      },
      "source": [
        "Label encode the categorical variables to make it easier to build algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiJX-TQavmZJ"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsPP8M8uwZNJ"
      },
      "source": [
        "df.location = le.fit_transform(df.location)\n",
        "df.rest_type = le.fit_transform(df.rest_type)\n",
        "df.cuisines = le.fit_transform(df.cuisines)\n",
        "df.menu_item = le.fit_transform(df.menu_item)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQal-5eiw8Pi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "4524b771-44ed-4139-8b3d-c943721154c0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>address</th>\n",
              "      <th>name</th>\n",
              "      <th>online_order</th>\n",
              "      <th>book_table</th>\n",
              "      <th>rate</th>\n",
              "      <th>votes</th>\n",
              "      <th>location</th>\n",
              "      <th>rest_type</th>\n",
              "      <th>dish_liked</th>\n",
              "      <th>cuisines</th>\n",
              "      <th>cost</th>\n",
              "      <th>reviews_list</th>\n",
              "      <th>menu_item</th>\n",
              "      <th>type</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.1</td>\n",
              "      <td>775</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
              "      <td>1386</td>\n",
              "      <td>800.0</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
              "      <td>5047</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
              "      <td>Spice Elephant</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>787</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
              "      <td>594</td>\n",
              "      <td>800.0</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
              "      <td>5047</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
              "      <td>San Churro Cafe</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>918</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
              "      <td>484</td>\n",
              "      <td>800.0</td>\n",
              "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
              "      <td>5047</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
              "      <td>Addhuri Udupi Bhojana</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>Masala Dosa</td>\n",
              "      <td>1587</td>\n",
              "      <td>300.0</td>\n",
              "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
              "      <td>5047</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
              "      <td>Grand Village</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>166</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>Panipuri, Gol Gappe</td>\n",
              "      <td>1406</td>\n",
              "      <td>600.0</td>\n",
              "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
              "      <td>5047</td>\n",
              "      <td>Buffet</td>\n",
              "      <td>Banashankari</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             address  ...          city\n",
              "0  942, 21st Main Road, 2nd Stage, Banashankari, ...  ...  Banashankari\n",
              "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...  ...  Banashankari\n",
              "2  1112, Next to KIMS Medical College, 17th Cross...  ...  Banashankari\n",
              "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  ...  Banashankari\n",
              "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...  ...  Banashankari\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyPgoO5LxASF"
      },
      "source": [
        "my_data=df.iloc[:,[2,3,4,5,6,7,9,10,12]]\n",
        "my_data.to_csv('Zomato_df.csv') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGWMrb0RxkP6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "24152a6f-7651-4da4-94fc-d012c7e42710"
      },
      "source": [
        "x = df.iloc[:,[2,3,5,6,7,9,10,12]]\n",
        "x.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>online_order</th>\n",
              "      <th>book_table</th>\n",
              "      <th>votes</th>\n",
              "      <th>location</th>\n",
              "      <th>rest_type</th>\n",
              "      <th>cuisines</th>\n",
              "      <th>cost</th>\n",
              "      <th>menu_item</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>775</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>1386</td>\n",
              "      <td>800.0</td>\n",
              "      <td>5047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>787</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>594</td>\n",
              "      <td>800.0</td>\n",
              "      <td>5047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>918</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>484</td>\n",
              "      <td>800.0</td>\n",
              "      <td>5047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>1587</td>\n",
              "      <td>300.0</td>\n",
              "      <td>5047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>166</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>1406</td>\n",
              "      <td>600.0</td>\n",
              "      <td>5047</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   online_order  book_table  votes  ...  cuisines   cost  menu_item\n",
              "0             1           1    775  ...      1386  800.0       5047\n",
              "1             1           0    787  ...       594  800.0       5047\n",
              "2             1           0    918  ...       484  800.0       5047\n",
              "3             0           0     88  ...      1587  300.0       5047\n",
              "4             0           0    166  ...      1406  600.0       5047\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51Tp_D3Qz884"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtmZmc8YxnER",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "8e1ad0a2-34f4-4444-e045-10a798d994eb"
      },
      "source": [
        "y = df['rate']\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        4.1\n",
              "1        4.1\n",
              "2        3.8\n",
              "3        3.7\n",
              "4        3.8\n",
              "        ... \n",
              "23243    3.8\n",
              "23244    3.9\n",
              "23245    2.8\n",
              "23246    2.5\n",
              "23247    4.3\n",
              "Name: rate, Length: 23248, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYshGo2k0IQu"
      },
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3,random_state=10)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W05zNBKdz4CZ"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## **Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXa3h0c_ykwu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9d8f609b-1474-4ddd-de98-e5e612740bb4"
      },
      "source": [
        "lr_model=LinearRegression()\n",
        "lr_model.fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHYDVDdp0CQ0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f4e30110-c73d-4ead-aa25-6136059cb604"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "y_pred=lr_model.predict(x_test)\n",
        "r2_score(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2281882852296705"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjQpqvsJ0_s3"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## **Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8mOXzV30gdE"
      },
      "source": [
        "#from sklearn.tree import DecisionTreeRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCd87Z7e1G4p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "359164c4-fec5-4fdf-a7cb-e101d64031a9"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "RF_Model=RandomForestRegressor(n_estimators=650,random_state=245,min_samples_leaf=.0001)\n",
        "RF_Model.fit(x_train,y_train)\n",
        "y_predict=RF_Model.predict(x_test)\n",
        "r2_score(y_test,y_predict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8812525999137639"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2_3HIv62jEF"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## **ExtraTree Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yI3dFTX52LhS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4cc44ed3-df61-4622-d5bd-c40fa47d0f1b"
      },
      "source": [
        "#Preparing Extra Tree Regression\n",
        "from sklearn.ensemble import  ExtraTreesRegressor\n",
        "ET_Model=ExtraTreesRegressor(n_estimators = 120)\n",
        "ET_Model.fit(x_train,y_train)\n",
        "y_predict=ET_Model.predict(x_test)\n",
        "\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test,y_predict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9326010735721576"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2m8oVS63GnX"
      },
      "source": [
        "Extra Tree Regressor gives us the best model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tr64VI0Q4PY7"
      },
      "source": [
        "Pickle: https://bit.ly/38MGdgn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VA6NmwL23ST"
      },
      "source": [
        "Use pickle to save our model so that we can use it later\n",
        "\n",
        "import pickle \n",
        "# Saving model to disk\n",
        "pickle.dump(ET_Model, open('model.pkl','wb'))\n",
        "model=pickle.load(open('model.pkl','rb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYJcpvFN5Jvo"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}