03 - Project - Classify House Prices.ipynb

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    "# Project: Classify House Prices\n",
    "- Put houses in price groups and try to predict based on Latitude and Longitude\n",
    "- That will show if the area is a good indicator of the house unit price"
   ]
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    "### Step 1: Import libraries"
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    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import r2_score, accuracy_score"
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    "### Step 2: Read the data\n",
    "- Use Pandas [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method to read **files/house_prices.csv**"
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    "### Step 3: Prepare data\n",
    "- Create 15 bins of house prices\n",
    "    - HINT: use [cut](https://pandas.pydata.org/docs/reference/api/pandas.cut.html) on the **'House unit price'** column with **bins=15** and assign the result to column **Class**.\n",
    "    - Get the category codes by transforming column **Class** with **.cat.codes** and assign it to **Class id**"
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    "### Step 4: Prepare training and test data\n",
    "- Assign **X** be all the data (it is needed in final step)\n",
    "- Assign **y** to be the **Class id** column.\n",
    "- Use **train_test_split** with **test_size=0.15**"
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    "### Step 5: Train a $k$-Neighbours Classifier\n",
    "- Create a model to **KNeighborsClassifier()**\n",
    "- Fit the model on **X_train[['Latitude', 'Longitude']]** and **y_train**\n",
    "- Predict **X_test[['Latitude', 'Longitude']]** and assign it to **y_pred**\n",
    "- Calculate the accuracy score"
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    "### Step 6: Make prediction of categories\n",
    "- Convert **y_pred** to a DataFrame\n",
    "    - HINT: **df_pred = pd.DataFrame(y_pred, columns=['Pred cat'])**\n",
    "- Get the middle value of the prediction category.\n",
    "    - HINT: **df_pred['Pred'] = df_pred['Pred cat'].apply(lambda x: X_test['Class'].cat.categories[x].mid)**\n",
    "- Calculate the **r2_score** of the predicted and real price **'House unit price'** of **X_test**"
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