00 - Project - k-Nearest-Neighbor Classifier (KNN).ipynb

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    "# Project - $k$-Nearest-Neighbors Classifier\n",
    "- Create a $k$-Nearest-Neighbors Classifier supporting 3 dimensions\n",
    "- Investigate whether it performs better"
   ]
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    "### Step 1: Import libraries"
   ]
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    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split"
   ]
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    "### Step 2: Read data\n",
    "- Use pandas [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method to read **files/weather.csv**\n",
    "- HINT: Use **parse_dates=True** and **index_col=0**"
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    "### Step 3: Investigate data types\n",
    "- Use [dtypes](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n",
    "- The goal is to identify all columns with datatype **float64** for next step"
   ]
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    "### Step 4: Choose 3 columns to create datasets\n",
    "- Use **Humidity3pm** and **Pressure3pm** together with another column to predict **RainTomorrow**\n",
    "- Make a list of three column names **'Humidity3pm', 'Pressure3pm', INSERT YOUR CHOICE** (should be one with dtype *float64*, e.g., **Cloud3pm**), and **'RainTomorrow'**\n",
    "- Create the dataset consisting of these 4 columns"
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    "### Step 5: Deal with remaining missing data\n",
    "- A simple choice is to simply remove rows with missing data\n",
    "- Use [dropna()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html)"
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    "### Step 6: Create training and test datasets\n",
    "- Define dataset **X** to be the data consisting of the three columns.\n",
    "- Define dataset **y** to be datset cosisting of **'RainTomorrow'**.\n",
    "    - HINT: Use list comprehension to transform **'No'** and **'Yes'** to 0 and 1, repectively (like in the Lesson)\n",
    "- Divide into **X_train, X_test, y_train, y_test** with **train_test_split**\n",
    "    - HINT: See how it is done in Lesson\n",
    "    - You can use **random_state=42** (or any other number) if you want to reproduce results."
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    "### Step 7: Train and test the model\n",
    "- Create classifier with **KNeighborsClassifier**\n",
    "    - You can play around with n_neighbors (default =5)\n",
    "- Fit the model with training data **(X_train, y_train**)\n",
    "- Predict data from **X_test** (use predict) and assign to **y_pred**.\n",
    "- Evalute score by using **metrics.accuracy_score(y_test, y_pred)**."
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    "### Step 8 (Optional): Try with different columns\n",
    "- You can redo with diffrent choise of columns (starting from step 4)"
   ]
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