{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "217d65b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the libraries needed for this implementation. The warnings module was introduced as a way to warn \n",
    "#programmers about changes in language or library features in anticipation of backwards incompatible changes coming \n",
    "#with Python 3.0. Since warnings are not fatal, a program may encounter the same warn-able situation many times in \n",
    "#the course of running a program\n",
    "#The filter is set to ignore so that the warnings are not displayed\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "056db896",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The main purpose of the OS module is to interact with the operating system\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83977081",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Boto3 is the name of the Python SDK for AWS. The module makes it easy to integrate the Python application, library, or script with AWS services including Amazon S3 and more\n",
    "#It allows for direct creation, updating and deletion of AWS resources from your Python scripts\n",
    "import boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a6e2ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Python io module allows us to manage the file-related input and output operations\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d201b566",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker\n",
    "import sagemaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "431d51e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For matrix operations and numerical processing\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff1d068f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for munging tabular data\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa20648e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For charts and visualizations \n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ecc9072",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scikit-learn is a machine learning library for Python. It features several regression, classification and clustering \n",
    "#algorithms including SVMs, gradient boosting, k-means, random forests and DBSCAN. . Using train_test_split() from the\n",
    "#data science library scikit-learn, you can split your dataset into subsets that minimize the potential for bias in \n",
    "#your evaluation and validation process.\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9718e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#With Scikit-Learn it is extremely straight forward to implement linear regression models, as all you really need to \n",
    "#do is import the LinearRegression class, instantiate it, and call the fit() method along with our training data.\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ca0e38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing other modules that makr it easier to work with the data\n",
    "import pickle,gzip,urllib,json,csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d460506",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The preprocessing module provides the StandardScaler utility class, which is a quick and easy way to perform the \n",
    "#many operations on an array-like dataset\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfc4d436",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the execution role for the notebook instance. This is the IAM role that you created for your notebook instance.\n",
    "#You pass the role to the tuning job.\n",
    "from sagemaker import get_execution_role\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "291a762b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resources represent an object-oriented interface to Amazon Web Services (AWS). They provide a higher-level \n",
    "#abstraction than the raw, low-level calls made by service clients. Defining the S3 resource, bucket name and csv\n",
    "#file object\n",
    "s3 = boto3.resource('s3')\n",
    "bucket_name = 'aws-machinelearning-chez'\n",
    "object_key = 'weatherhistory2.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3ca18e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use the boto3 client to retrieve the file from bucket aws-machinelearning-chez named weatherhistory.csv. \n",
    "#Define Weather dataset as the csv file read in the response from the get_object operation\n",
    "s3_client = boto3.client('s3')\n",
    "response = s3_client.get_object(Bucket=bucket_name, Key=object_key)\n",
    "response_body = response[\"Body\"].read()\n",
    "weather_df = pd.read_csv(io.BytesIO(response_body), header=0, delimiter=\",\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b43f2087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Formatted Date</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-04-01 00:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-04-01 01:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-04-01 02:00:00.000 +0200</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-04-01 03:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-04-01 04:00:00.000 +0200</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Formatted Date        Summary Precip Type  Temperature (C)  \\\n",
       "0  2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "1  2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2  2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "3  2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain         8.288889   \n",
       "4  2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain         8.755556   \n",
       "\n",
       "   Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "0                  7.388889      0.89            14.1197   \n",
       "1                  7.227778      0.86            14.2646   \n",
       "2                  9.377778      0.89             3.9284   \n",
       "3                  5.944444      0.83            14.1036   \n",
       "4                  6.977778      0.83            11.0446   \n",
       "\n",
       "   Wind Bearing (degrees)  Visibility (km)  Loud Cover  Pressure (millibars)  \\\n",
       "0                     251          15.8263           0               1015.13   \n",
       "1                     259          15.8263           0               1015.63   \n",
       "2                     204          14.9569           0               1015.94   \n",
       "3                     269          15.8263           0               1016.41   \n",
       "4                     259          15.8263           0               1016.51   \n",
       "\n",
       "                       Daily Summary  \n",
       "0  Partly cloudy throughout the day.  \n",
       "1  Partly cloudy throughout the day.  \n",
       "2  Partly cloudy throughout the day.  \n",
       "3  Partly cloudy throughout the day.  \n",
       "4  Partly cloudy throughout the day.  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the data\n",
    "weather_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad2e2238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Formatted Date', 'Summary', 'Precip Type', 'Temperature (C)',\n",
       "       'Apparent Temperature (C)', 'Humidity', 'Wind Speed (km/h)',\n",
       "       'Wind Bearing (degrees)', 'Visibility (km)', 'Loud Cover',\n",
       "       'Pressure (millibars)', 'Daily Summary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gives an array of colums names \n",
    "weather_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "748178d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.0</td>\n",
       "      <td>96453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.932678</td>\n",
       "      <td>10.855029</td>\n",
       "      <td>0.734899</td>\n",
       "      <td>10.810640</td>\n",
       "      <td>187.509232</td>\n",
       "      <td>10.347325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1003.235956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.551546</td>\n",
       "      <td>10.696847</td>\n",
       "      <td>0.195473</td>\n",
       "      <td>6.913571</td>\n",
       "      <td>107.383428</td>\n",
       "      <td>4.192123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.969906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-21.822222</td>\n",
       "      <td>-27.716667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.688889</td>\n",
       "      <td>2.311111</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.828200</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>8.339800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>9.965900</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>10.046400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.838889</td>\n",
       "      <td>18.838889</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>14.135800</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>14.812000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.905556</td>\n",
       "      <td>39.344444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.852600</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1046.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature (C)  Apparent Temperature (C)      Humidity  \\\n",
       "count     96453.000000              96453.000000  96453.000000   \n",
       "mean         11.932678                 10.855029      0.734899   \n",
       "std           9.551546                 10.696847      0.195473   \n",
       "min         -21.822222                -27.716667      0.000000   \n",
       "25%           4.688889                  2.311111      0.600000   \n",
       "50%          12.000000                 12.000000      0.780000   \n",
       "75%          18.838889                 18.838889      0.890000   \n",
       "max          39.905556                 39.344444      1.000000   \n",
       "\n",
       "       Wind Speed (km/h)  Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "count       96453.000000            96453.000000     96453.000000     96453.0   \n",
       "mean           10.810640              187.509232        10.347325         0.0   \n",
       "std             6.913571              107.383428         4.192123         0.0   \n",
       "min             0.000000                0.000000         0.000000         0.0   \n",
       "25%             5.828200              116.000000         8.339800         0.0   \n",
       "50%             9.965900              180.000000        10.046400         0.0   \n",
       "75%            14.135800              290.000000        14.812000         0.0   \n",
       "max            63.852600              359.000000        16.100000         0.0   \n",
       "\n",
       "       Pressure (millibars)  \n",
       "count          96453.000000  \n",
       "mean            1003.235956  \n",
       "std              116.969906  \n",
       "min                0.000000  \n",
       "25%             1011.900000  \n",
       "50%             1016.450000  \n",
       "75%             1021.090000  \n",
       "max             1046.380000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate descriptive statistics that summarize the central tendency, dispersion and shape of the dataset’s \n",
    "#distribution excluding NaN values\n",
    "weather_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "59c838b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature (C)</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>11.932678</td>\n",
       "      <td>9.551546</td>\n",
       "      <td>-21.822222</td>\n",
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       "      <td>12.0000</td>\n",
       "      <td>18.838889</td>\n",
       "      <td>39.905556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>10.855029</td>\n",
       "      <td>10.696847</td>\n",
       "      <td>-27.716667</td>\n",
       "      <td>2.311111</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>18.838889</td>\n",
       "      <td>39.344444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>0.734899</td>\n",
       "      <td>0.195473</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>10.810640</td>\n",
       "      <td>6.913571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.828200</td>\n",
       "      <td>9.9659</td>\n",
       "      <td>14.135800</td>\n",
       "      <td>63.852600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>187.509232</td>\n",
       "      <td>107.383428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>180.0000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>359.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visibility (km)</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>10.347325</td>\n",
       "      <td>4.192123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.339800</td>\n",
       "      <td>10.0464</td>\n",
       "      <td>14.812000</td>\n",
       "      <td>16.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loud Cover</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <td>96453.0</td>\n",
       "      <td>1003.235956</td>\n",
       "      <td>116.969906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1011.900000</td>\n",
       "      <td>1016.4500</td>\n",
       "      <td>1021.090000</td>\n",
       "      <td>1046.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count         mean         std        min  \\\n",
       "Temperature (C)           96453.0    11.932678    9.551546 -21.822222   \n",
       "Apparent Temperature (C)  96453.0    10.855029   10.696847 -27.716667   \n",
       "Humidity                  96453.0     0.734899    0.195473   0.000000   \n",
       "Wind Speed (km/h)         96453.0    10.810640    6.913571   0.000000   \n",
       "Wind Bearing (degrees)    96453.0   187.509232  107.383428   0.000000   \n",
       "Visibility (km)           96453.0    10.347325    4.192123   0.000000   \n",
       "Loud Cover                96453.0     0.000000    0.000000   0.000000   \n",
       "Pressure (millibars)      96453.0  1003.235956  116.969906   0.000000   \n",
       "\n",
       "                                  25%        50%          75%          max  \n",
       "Temperature (C)              4.688889    12.0000    18.838889    39.905556  \n",
       "Apparent Temperature (C)     2.311111    12.0000    18.838889    39.344444  \n",
       "Humidity                     0.600000     0.7800     0.890000     1.000000  \n",
       "Wind Speed (km/h)            5.828200     9.9659    14.135800    63.852600  \n",
       "Wind Bearing (degrees)     116.000000   180.0000   290.000000   359.000000  \n",
       "Visibility (km)              8.339800    10.0464    14.812000    16.100000  \n",
       "Loud Cover                   0.000000     0.0000     0.000000     0.000000  \n",
       "Pressure (millibars)      1011.900000  1016.4500  1021.090000  1046.380000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e8c1115",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A seaborn plot enables us to visualize a statistical plot of the raw data\n",
    "import seaborn\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.tight_layout()\n",
    "seaborn.distplot(weather_df['Apparent Temperature (C)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d82a835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96453 entries, 0 to 96452\n",
      "Data columns (total 12 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Formatted Date            96453 non-null  object \n",
      " 1   Summary                   96453 non-null  object \n",
      " 2   Precip Type               95936 non-null  object \n",
      " 3   Temperature (C)           96453 non-null  float64\n",
      " 4   Apparent Temperature (C)  96453 non-null  float64\n",
      " 5   Humidity                  96453 non-null  float64\n",
      " 6   Wind Speed (km/h)         96453 non-null  float64\n",
      " 7   Wind Bearing (degrees)    96453 non-null  int64  \n",
      " 8   Visibility (km)           96453 non-null  float64\n",
      " 9   Loud Cover                96453 non-null  int64  \n",
      " 10  Pressure (millibars)      96453 non-null  float64\n",
      " 11  Daily Summary             96453 non-null  object \n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Prints a concise summary of the dataframe\n",
    "weather_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c246ca16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date              False\n",
       "Summary                     False\n",
       "Precip Type                  True\n",
       "Temperature (C)             False\n",
       "Apparent Temperature (C)    False\n",
       "Humidity                    False\n",
       "Wind Speed (km/h)           False\n",
       "Wind Bearing (degrees)      False\n",
       "Visibility (km)             False\n",
       "Loud Cover                  False\n",
       "Pressure (millibars)        False\n",
       "Daily Summary               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is any null value in a particular column\n",
    "weather_df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6c9c0621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date              False\n",
       "Summary                     False\n",
       "Precip Type                 False\n",
       "Temperature (C)             False\n",
       "Apparent Temperature (C)    False\n",
       "Humidity                    False\n",
       "Wind Speed (km/h)           False\n",
       "Wind Bearing (degrees)      False\n",
       "Visibility (km)             False\n",
       "Loud Cover                  False\n",
       "Pressure (millibars)        False\n",
       "Daily Summary               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there are all null values in a particular column\n",
    "weather_df.isnull().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a09da4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date              0.00\n",
       "Summary                     0.00\n",
       "Precip Type                 0.54\n",
       "Temperature (C)             0.00\n",
       "Apparent Temperature (C)    0.00\n",
       "Humidity                    0.00\n",
       "Wind Speed (km/h)           0.00\n",
       "Wind Bearing (degrees)      0.00\n",
       "Visibility (km)             0.00\n",
       "Loud Cover                  0.00\n",
       "Pressure (millibars)        0.00\n",
       "Daily Summary               0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of null values in each column\n",
    "round(100*(weather_df.isnull().sum()/len(weather_df.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b0903331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rain    85224\n",
       "snow    10712\n",
       "Name: Precip Type, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#determine what values are in this column and what the count is of each value\n",
    "weather_df['Precip Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cbc2584b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handling missing values by imputation of the mode i.e. rain\n",
    "weather_df.loc[weather_df['Precip Type'].isnull(),'Precip Type']='rain'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9ff40c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date              0.0\n",
       "Summary                     0.0\n",
       "Precip Type                 0.0\n",
       "Temperature (C)             0.0\n",
       "Apparent Temperature (C)    0.0\n",
       "Humidity                    0.0\n",
       "Wind Speed (km/h)           0.0\n",
       "Wind Bearing (degrees)      0.0\n",
       "Visibility (km)             0.0\n",
       "Loud Cover                  0.0\n",
       "Pressure (millibars)        0.0\n",
       "Daily Summary               0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of null values in each column for the second time\n",
    "round(100*(weather_df.isnull().sum()/len(weather_df.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0b15207d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Input binary values in Precip type column for rain and snow\n",
    "weather_df.loc[weather_df['Precip Type']=='rain','Precip Type']=1\n",
    "weather_df.loc[weather_df['Precip Type']=='snow','Precip Type']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1fd8dc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df_num=weather_df[list(weather_df.dtypes[weather_df.dtypes!='odject'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2f744270",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop columns that are irrelevant to the model training\n",
    "to_drop = ['Formatted Date','Summary','Daily Summary']\n",
    "weather_df.drop(to_drop, inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dc3b792f",
   "metadata": {},
   "outputs": [
    {
     "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>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1015.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1015.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0</td>\n",
       "      <td>1015.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1016.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1016.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Precip Type  Temperature (C)  Apparent Temperature (C)  Humidity  \\\n",
       "0           1         9.472222                  7.388889      0.89   \n",
       "1           1         9.355556                  7.227778      0.86   \n",
       "2           1         9.377778                  9.377778      0.89   \n",
       "3           1         8.288889                  5.944444      0.83   \n",
       "4           1         8.755556                  6.977778      0.83   \n",
       "\n",
       "   Wind Speed (km/h)  Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "0            14.1197                     251          15.8263           0   \n",
       "1            14.2646                     259          15.8263           0   \n",
       "2             3.9284                     204          14.9569           0   \n",
       "3            14.1036                     269          15.8263           0   \n",
       "4            11.0446                     259          15.8263           0   \n",
       "\n",
       "   Pressure (millibars)  \n",
       "0               1015.13  \n",
       "1               1015.63  \n",
       "2               1015.94  \n",
       "3               1016.41  \n",
       "4               1016.51  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff648831",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df_num=weather_df[list(weather_df.dtypes[weather_df.dtypes!='odject'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3bc98770",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating the training and the test datasets. The feature that we are predicting is temperature, so we separate \n",
    "#it out into a column on its own (weather_y) leaving the rest of the data in weather_x\n",
    "weather_df_y = weather_df_num.pop('Temperature (C)')\n",
    "weather_df_x = weather_df_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62fe9537",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In supervised machine learning, we add data to the model and tell it what the output will be based on the various \n",
    "#conditions of that data. The model then learns from these features and is then able to make a prediction for new \n",
    "#values \n",
    "#So we now split the data into train and test\n",
    "\n",
    "train_x,test_x,train_y,test_y = train_test_split(weather_df_x,weather_df_y,test_size = 0.2,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "56acf2bc",
   "metadata": {},
   "outputs": [
    {
     "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>Precip Type</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70626</th>\n",
       "      <td>1</td>\n",
       "      <td>21.061111</td>\n",
       "      <td>0.31</td>\n",
       "      <td>12.5580</td>\n",
       "      <td>110</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1005.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52457</th>\n",
       "      <td>1</td>\n",
       "      <td>25.016667</td>\n",
       "      <td>0.36</td>\n",
       "      <td>18.4989</td>\n",
       "      <td>352</td>\n",
       "      <td>10.3523</td>\n",
       "      <td>0</td>\n",
       "      <td>1025.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90690</th>\n",
       "      <td>1</td>\n",
       "      <td>0.738889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>17.1304</td>\n",
       "      <td>270</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1014.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69528</th>\n",
       "      <td>1</td>\n",
       "      <td>13.772222</td>\n",
       "      <td>0.78</td>\n",
       "      <td>14.4900</td>\n",
       "      <td>300</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0</td>\n",
       "      <td>1014.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92419</th>\n",
       "      <td>1</td>\n",
       "      <td>23.288889</td>\n",
       "      <td>0.82</td>\n",
       "      <td>6.3917</td>\n",
       "      <td>357</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1022.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Precip Type  Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "70626           1                 21.061111      0.31            12.5580   \n",
       "52457           1                 25.016667      0.36            18.4989   \n",
       "90690           1                  0.738889      0.89            17.1304   \n",
       "69528           1                 13.772222      0.78            14.4900   \n",
       "92419           1                 23.288889      0.82             6.3917   \n",
       "\n",
       "       Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "70626                     110          16.1000           0   \n",
       "52457                     352          10.3523           0   \n",
       "90690                     270          15.8263           0   \n",
       "69528                     300          15.8263           0   \n",
       "92419                     357          16.1000           0   \n",
       "\n",
       "       Pressure (millibars)  \n",
       "70626               1005.87  \n",
       "52457               1025.36  \n",
       "90690               1014.75  \n",
       "69528               1014.56  \n",
       "92419               1022.05  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the training dataset. It has all columns except the temperature column\n",
    "train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f30439e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70626    21.061111\n",
       "52457    25.016667\n",
       "90690     4.422222\n",
       "69528    13.772222\n",
       "92419    23.288889\n",
       "Name: Temperature (C), dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a936d649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37443    -2.288889\n",
       "86534     8.861111\n",
       "2082      9.805556\n",
       "53130    27.222222\n",
       "45196    17.705556\n",
       "Name: Temperature (C), dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16b30e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19291, 8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6e7af2d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77162, 8)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f97f3a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using polynomial regression to train the model. Polynomial Regression is a one of the types of linear regression in \n",
    "#which the relationship between the independent variable x and dependent variable y is modeled as an nth degree \n",
    "#polynomial. In this case that degree is 4\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "794b8782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PolynomialFeatures(degree=4)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we train the model\n",
    "X_poly = poly.fit_transform(train_x)\n",
    "poly.fit(X_poly, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "033f87aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we use Decision Tree Regressor to train the model\n",
    "#Decision tree builds regression or classification models in the form of a tree structure. It breaks down a dataset\n",
    "#into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. \n",
    "#The final result is a tree with decision nodes and leaf nodes. \n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "regressor.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a3cc05b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00803046229678356"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We now look at how well out model has been trained by feeding it test data and getting it to make a prediction and\n",
    "#then comparing its output to test data output\n",
    "\n",
    "prediction3 = regressor.predict(test_x)\n",
    "np.mean((prediction3-test_y)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "278387c1",
   "metadata": {},
   "outputs": [
    {
     "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>actual</th>\n",
       "      <th>prediction</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37443</th>\n",
       "      <td>-2.288889</td>\n",
       "      <td>-2.266667</td>\n",
       "      <td>-2.222222e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86534</th>\n",
       "      <td>8.861111</td>\n",
       "      <td>8.855556</td>\n",
       "      <td>5.555555e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>9.805556</td>\n",
       "      <td>9.811111</td>\n",
       "      <td>-5.555555e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53130</th>\n",
       "      <td>27.222222</td>\n",
       "      <td>27.222222</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45196</th>\n",
       "      <td>17.705556</td>\n",
       "      <td>17.705556</td>\n",
       "      <td>3.552714e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55387</th>\n",
       "      <td>-10.066667</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-6.666667e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25976</th>\n",
       "      <td>9.972222</td>\n",
       "      <td>9.972222</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32423</th>\n",
       "      <td>12.777778</td>\n",
       "      <td>12.777778</td>\n",
       "      <td>-6.217249e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26570</th>\n",
       "      <td>15.277778</td>\n",
       "      <td>15.277778</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45834</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>4.440892e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19291 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          actual  prediction          diff\n",
       "37443  -2.288889   -2.266667 -2.222222e-02\n",
       "86534   8.861111    8.855556  5.555555e-03\n",
       "2082    9.805556    9.811111 -5.555555e-03\n",
       "53130  27.222222   27.222222  0.000000e+00\n",
       "45196  17.705556   17.705556  3.552714e-15\n",
       "...          ...         ...           ...\n",
       "55387 -10.066667  -10.000000 -6.666667e-02\n",
       "25976   9.972222    9.972222  0.000000e+00\n",
       "32423  12.777778   12.777778 -6.217249e-14\n",
       "26570  15.277778   15.277778  0.000000e+00\n",
       "45834   2.200000    2.200000  4.440892e-16\n",
       "\n",
       "[19291 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now look at the difference between predicted and actual\n",
    "pd.DataFrame({'actual':test_y,\n",
    "             'prediction':prediction3,\n",
    "             'diff':(test_y-prediction3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3914b5fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now look at the difference between predicted and actual\n",
    "df=pd.DataFrame({'actual':test_y,\n",
    "             'prediction':prediction3,\n",
    "             'diff':(test_y-prediction3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b82a4136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1= df.head(25)\n",
    "df1.plot(kind='bar', figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-',linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':',linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "23224eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import metrics library\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2780f7e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.021708741547046377\n"
     ]
    }
   ],
   "source": [
    "#Print results of MAE\n",
    "print(metrics.mean_absolute_error(test_y, prediction3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "860e7137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00803046229678357\n"
     ]
    }
   ],
   "source": [
    "#Print results of MSE\n",
    "print(metrics.mean_squared_error(test_y, prediction3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "edbe23f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08961284671732937\n"
     ]
    }
   ],
   "source": [
    "#Print results of RMSE\n",
    "print(np.sqrt(metrics.mean_squared_error(test_y, prediction3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fb7ce592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=10, random_state=0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Using Random Forest to train our model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regr = RandomForestRegressor(max_depth=10,random_state=0,n_estimators=100)\n",
    "regr.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e2afeb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008141637115617098"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We now look at how well out model has been trained by feeding it test data and getting it to make a prediction and\n",
    "#then comparing its output to test data output\n",
    "\n",
    "prediction4 = regr.predict(test_x)\n",
    "np.mean((prediction4-test_y)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "57855a0b",
   "metadata": {},
   "outputs": [
    {
     "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>actual</th>\n",
       "      <th>prediction</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37443</th>\n",
       "      <td>-2.288889</td>\n",
       "      <td>-2.482177</td>\n",
       "      <td>0.193288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86534</th>\n",
       "      <td>8.861111</td>\n",
       "      <td>8.892260</td>\n",
       "      <td>-0.031149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>9.805556</td>\n",
       "      <td>9.847246</td>\n",
       "      <td>-0.041690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53130</th>\n",
       "      <td>27.222222</td>\n",
       "      <td>27.203396</td>\n",
       "      <td>0.018826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45196</th>\n",
       "      <td>17.705556</td>\n",
       "      <td>17.713716</td>\n",
       "      <td>-0.008160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55387</th>\n",
       "      <td>-10.066667</td>\n",
       "      <td>-10.071566</td>\n",
       "      <td>0.004899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25976</th>\n",
       "      <td>9.972222</td>\n",
       "      <td>9.913953</td>\n",
       "      <td>0.058269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32423</th>\n",
       "      <td>12.777778</td>\n",
       "      <td>12.781718</td>\n",
       "      <td>-0.003940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26570</th>\n",
       "      <td>15.277778</td>\n",
       "      <td>15.275648</td>\n",
       "      <td>0.002130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45834</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.187450</td>\n",
       "      <td>0.012550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19291 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          actual  prediction      diff\n",
       "37443  -2.288889   -2.482177  0.193288\n",
       "86534   8.861111    8.892260 -0.031149\n",
       "2082    9.805556    9.847246 -0.041690\n",
       "53130  27.222222   27.203396  0.018826\n",
       "45196  17.705556   17.713716 -0.008160\n",
       "...          ...         ...       ...\n",
       "55387 -10.066667  -10.071566  0.004899\n",
       "25976   9.972222    9.913953  0.058269\n",
       "32423  12.777778   12.781718 -0.003940\n",
       "26570  15.277778   15.275648  0.002130\n",
       "45834   2.200000    2.187450  0.012550\n",
       "\n",
       "[19291 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now look at the difference between predicted and actual\n",
    "pd.DataFrame({'actual':test_y,\n",
    "             'prediction':prediction4,\n",
    "             'diff':(test_y-prediction4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "af4663b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now look at the difference between predicted and actual\n",
    "df2=pd.DataFrame({'actual':test_y,\n",
    "             'prediction':prediction4,\n",
    "             'diff':(test_y-prediction4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5413c801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1= df2.head(25)\n",
    "df1.plot(kind='bar', figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-',linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':',linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a3e3ce21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0432385808682435\n"
     ]
    }
   ],
   "source": [
    "#Print results of MAE for Random Forest\n",
    "print(metrics.mean_absolute_error(test_y, prediction4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f38df479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008141637115617032\n"
     ]
    }
   ],
   "source": [
    "#Print results of MSE for Random Forest\n",
    "print(metrics.mean_squared_error(test_y, prediction4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "39a5e097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09023102080558011\n"
     ]
    }
   ],
   "source": [
    "#Print results of RMSE for Random Forest\n",
    "print(np.sqrt(metrics.mean_squared_error(test_y, prediction4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c3359c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}