weather-prediction-model-precision-comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1f99117",
   "metadata": {
    "papermill": {
     "duration": 0.018583,
     "end_time": "2022-01-17T18:28:23.555057",
     "exception": false,
     "start_time": "2022-01-17T18:28:23.536474",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Weather Forecasting**\n",
    "\n",
    "Importing some important utilities!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f3b5a08",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:23.593738Z",
     "iopub.status.busy": "2022-01-17T18:28:23.592438Z",
     "iopub.status.idle": "2022-01-17T18:28:24.680688Z",
     "shell.execute_reply": "2022-01-17T18:28:24.679970Z",
     "shell.execute_reply.started": "2022-01-17T18:26:27.782932Z"
    },
    "papermill": {
     "duration": 1.10893,
     "end_time": "2022-01-17T18:28:24.680910",
     "exception": false,
     "start_time": "2022-01-17T18:28:23.571980",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/weather-prediction/seattle-weather.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0a15ca8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:24.720626Z",
     "iopub.status.busy": "2022-01-17T18:28:24.719926Z",
     "iopub.status.idle": "2022-01-17T18:28:24.738484Z",
     "shell.execute_reply": "2022-01-17T18:28:24.737856Z",
     "shell.execute_reply.started": "2022-01-17T18:26:28.020077Z"
    },
    "papermill": {
     "duration": 0.040304,
     "end_time": "2022-01-17T18:28:24.738646",
     "exception": false,
     "start_time": "2022-01-17T18:28:24.698342",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../input/weather-prediction/seattle-weather.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22850560",
   "metadata": {
    "papermill": {
     "duration": 0.0164,
     "end_time": "2022-01-17T18:28:24.771982",
     "exception": false,
     "start_time": "2022-01-17T18:28:24.755582",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Checking whether the dataset got imported to the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8338073a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:24.809039Z",
     "iopub.status.busy": "2022-01-17T18:28:24.808356Z",
     "iopub.status.idle": "2022-01-17T18:28:24.815963Z",
     "shell.execute_reply": "2022-01-17T18:28:24.816486Z",
     "shell.execute_reply.started": "2022-01-17T18:26:28.431264Z"
    },
    "papermill": {
     "duration": 0.027906,
     "end_time": "2022-01-17T18:28:24.816651",
     "exception": false,
     "start_time": "2022-01-17T18:28:24.788745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1461, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b296fa7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:24.854271Z",
     "iopub.status.busy": "2022-01-17T18:28:24.853574Z",
     "iopub.status.idle": "2022-01-17T18:28:24.873794Z",
     "shell.execute_reply": "2022-01-17T18:28:24.874328Z",
     "shell.execute_reply.started": "2022-01-17T18:26:28.655704Z"
    },
    "papermill": {
     "duration": 0.040555,
     "end_time": "2022-01-17T18:28:24.874504",
     "exception": false,
     "start_time": "2022-01-17T18:28:24.833949",
     "status": "completed"
    },
    "tags": []
   },
   "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>date</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>wind</th>\n",
       "      <th>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>drizzle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>20.3</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  precipitation  temp_max  temp_min  wind  weather\n",
       "0  2012-01-01            0.0      12.8       5.0   4.7  drizzle\n",
       "1  2012-01-02           10.9      10.6       2.8   4.5     rain\n",
       "2  2012-01-03            0.8      11.7       7.2   2.3     rain\n",
       "3  2012-01-04           20.3      12.2       5.6   4.7     rain\n",
       "4  2012-01-05            1.3       8.9       2.8   6.1     rain"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e31c1ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:24.915262Z",
     "iopub.status.busy": "2022-01-17T18:28:24.914568Z",
     "iopub.status.idle": "2022-01-17T18:28:24.945122Z",
     "shell.execute_reply": "2022-01-17T18:28:24.945644Z",
     "shell.execute_reply.started": "2022-01-17T18:26:28.863043Z"
    },
    "papermill": {
     "duration": 0.052648,
     "end_time": "2022-01-17T18:28:24.945844",
     "exception": false,
     "start_time": "2022-01-17T18:28:24.893196",
     "status": "completed"
    },
    "tags": []
   },
   "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>precipitation</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1461.000000</td>\n",
       "      <td>1461.000000</td>\n",
       "      <td>1461.000000</td>\n",
       "      <td>1461.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.029432</td>\n",
       "      <td>16.439083</td>\n",
       "      <td>8.234771</td>\n",
       "      <td>3.241136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.680194</td>\n",
       "      <td>7.349758</td>\n",
       "      <td>5.023004</td>\n",
       "      <td>1.437825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.600000</td>\n",
       "      <td>-7.100000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.800000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>55.900000</td>\n",
       "      <td>35.600000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       precipitation     temp_max     temp_min         wind\n",
       "count    1461.000000  1461.000000  1461.000000  1461.000000\n",
       "mean        3.029432    16.439083     8.234771     3.241136\n",
       "std         6.680194     7.349758     5.023004     1.437825\n",
       "min         0.000000    -1.600000    -7.100000     0.400000\n",
       "25%         0.000000    10.600000     4.400000     2.200000\n",
       "50%         0.000000    15.600000     8.300000     3.000000\n",
       "75%         2.800000    22.200000    12.200000     4.000000\n",
       "max        55.900000    35.600000    18.300000     9.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9199357",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:24.986316Z",
     "iopub.status.busy": "2022-01-17T18:28:24.985541Z",
     "iopub.status.idle": "2022-01-17T18:28:25.001327Z",
     "shell.execute_reply": "2022-01-17T18:28:25.001911Z",
     "shell.execute_reply.started": "2022-01-17T18:26:29.075592Z"
    },
    "papermill": {
     "duration": 0.037571,
     "end_time": "2022-01-17T18:28:25.002095",
     "exception": false,
     "start_time": "2022-01-17T18:28:24.964524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1461 entries, 0 to 1460\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           1461 non-null   object \n",
      " 1   precipitation  1461 non-null   float64\n",
      " 2   temp_max       1461 non-null   float64\n",
      " 3   temp_min       1461 non-null   float64\n",
      " 4   wind           1461 non-null   float64\n",
      " 5   weather        1461 non-null   object \n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 68.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7170b5b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:25.042985Z",
     "iopub.status.busy": "2022-01-17T18:28:25.042234Z",
     "iopub.status.idle": "2022-01-17T18:28:25.050362Z",
     "shell.execute_reply": "2022-01-17T18:28:25.051172Z",
     "shell.execute_reply.started": "2022-01-17T18:26:29.270323Z"
    },
    "papermill": {
     "duration": 0.030259,
     "end_time": "2022-01-17T18:28:25.051393",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.021134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "precipitation    0\n",
       "temp_max         0\n",
       "temp_min         0\n",
       "wind             0\n",
       "weather          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5033ab6",
   "metadata": {
    "papermill": {
     "duration": 0.01838,
     "end_time": "2022-01-17T18:28:25.089311",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.070931",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are no null values in the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7abbe40",
   "metadata": {
    "papermill": {
     "duration": 0.018787,
     "end_time": "2022-01-17T18:28:25.127355",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.108568",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Pre-Processing:\n",
    "The column weather contains the data value in the string form and we need to predict the weather data so for that we need to process the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9c58d80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:25.169436Z",
     "iopub.status.busy": "2022-01-17T18:28:25.168474Z",
     "iopub.status.idle": "2022-01-17T18:28:25.313363Z",
     "shell.execute_reply": "2022-01-17T18:28:25.312694Z",
     "shell.execute_reply.started": "2022-01-17T18:26:29.864666Z"
    },
    "papermill": {
     "duration": 0.167064,
     "end_time": "2022-01-17T18:28:25.313519",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.146455",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df['weather']=LabelEncoder().fit_transform(df['weather'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e6fb39",
   "metadata": {
    "papermill": {
     "duration": 0.018578,
     "end_time": "2022-01-17T18:28:25.352428",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.333850",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6838e1f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:25.399815Z",
     "iopub.status.busy": "2022-01-17T18:28:25.399116Z",
     "iopub.status.idle": "2022-01-17T18:28:25.720028Z",
     "shell.execute_reply": "2022-01-17T18:28:25.719453Z",
     "shell.execute_reply.started": "2022-01-17T18:26:30.270184Z"
    },
    "papermill": {
     "duration": 0.348987,
     "end_time": "2022-01-17T18:28:25.720179",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.371192",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcsAAAFqCAYAAAB1QnEMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAlsUlEQVR4nO3debgkdXno8e/LAA4gm+DKgAOIGESECAghGkAwmCg8UVwwqIg6MQbEHRLvZVFzccWIl6jDalDjgkYGRVERxCuLM2zDosSRRTaDICIKAsN57x9VR3qOc053U1Vzuqq/n3nqObV11ds15/Tbv6V+FZmJJEma3mqzHYAkSaPOZClJUh8mS0mS+jBZSpLUh8lSkqQ+TJaSJPVhspQktUZEnBIRd0TE1dNsj4g4PiKWRcTSiPjzOs5rspQktclpwD4zbH8RsFU5LQA+VcdJTZaSpNbIzAuAX8+wy37Af2ThYmCDiHhy1fOaLCVJXbIJcHPP8i3lukpWr3qAQTx05/WOqTeDDTd7wWyHMLKeveHmsx3CyPrX5RvNdggjba+7L5ztEEbW8gdvjaaOXfXzfs3Hb/kPFNWnkxZm5sJqUVW3SpKlJGlMTDxc6eVlYqySHG8FNu1Znleuq8RqWElSfXKi2lTdIuC1Za/YXYB7MvP2qge1ZClJao2I+E9gd2DjiLgFOApYAyAzPw2cDfwNsAy4D3h9HecdOFlGxEuBDwFPAKKcMjPXqyMQSVIHTNRSOpxWZh7QZ3sC/1T3eYcpWX4YeElm/qTuICRJ3ZD1VKWOnGGS5f+YKCVJM2q4ZDlbhkmWSyLiS8DXgQcmV2bm1+oOSpLUUpYsWY+isfSFPesSMFlKkjpt4GSZmbX0KJIkdVjF+yxH1cD3WUbEvIj4r3K09zsi4qsRMa/J4CRJLTP791k2YphBCU6luNnzKeV0VrlOkqTCxES1aUQNkywfn5mnZubycjoNeHxDcUmSWihzotI0qoZJlndFxIERMaecDgTuaiowSZJGxTDJ8mDgFcAvgduB/alpGCFJUkd0tBp2mN6wNwH7NhiLJKntRrgqtYq+yTIi3pOZH46IT1LcV7mCzHxrI5FJktqno7eODFKynBzibkmTgUiSNKr6JsvMPKucvS8zv9K7LSJe3khUkqR26mg17DAdfP55wHWSpHE1rh18IuJFFA/S3CQiju/ZtB6wvKnAJEkt1NGS5SBtlrdRtFfuC1zas/5e4O1NBCVJaqkRLh1WMUib5ZXAlRHxhcx8aBXEJEnSSBnmEV3zI+JYYBtg7uTKzNyi9qgkSa2UOb63jkw6FTgK+DiwB8XoPcN0EJIkdV1H2yyHSXZrZea5QGTmTZl5NPC3zYQlSWqlce0N2+OBiFgN+FlEHALcCjy2mbAkSa1kyZLDgLWBtwLPAQ4EXttEUJIkjZKhOvhk5mLgd5RPGylH8LmkicAkSS3U0bFhHcFHklSfnKg2jShH8JEk1WeEO+lU4Qg+kiT1McwIPp/PzIFLkhGxAFgA8O8f+wBvfO0Bjz5KSVI7jHBVahWDVMN+OTNfAVweESt7+PN2K3tdZi4EFgI8dOf1f/I6SVIHjXE17GHlzxc3GYgkqQPGNVlm5u3lz5si4knAzkACizPzlw3HJ0lqka6ODTvwrSMR8Ubgx8BLgf2BiyPi4KYCkyRpVAwzKMG7gR0y8y6AiNgIuBA4pYnAJEktNK7VsD3uorhdZNK95TpJkgrj2hu2xzLgkog4k6LNcj9gaUS8AyAzj2sgPklSm1iy5OflNOnM8ue69YUjSdLoGThZZuYxTQYiSeqAca2GjYh/y8y3RcRZFNWvK8jMfRuJTJLUPmNcDXt6+fOjTQYiSeqAcS1ZZubk4OlLgPsziysREXOAxzQYmySpbTpashzmeZbnAmv3LK8FfK/ecCRJGj3D9Iadm5m/m1zIzN9FxNozvUCSNGYsWfL7iPjzyYWIeA5wf/0hSZJaKyeqTSNqmJLl24CvRMRtQABPAl7ZRFCSpJZquGQZEfsAnwDmACdl5genbN8M+CywQbnPEZl5dtXzDnOf5eKIeAawdbnqusx8qGoAkqQOabB0WHYsPQHYG7gFWBwRizLz2p7d/hfw5cz8VERsA5wNzK967mGeOrI2cDhwWGZeDcyPCJ9xKUlaVXYGlmXm9Zn5IPBFiqFXeyWwXjm/PnBbHSceps3yVOBBYNdy+VbgA3UEIUnqiImJSlNELIiIJT3Tgp6jbwLc3LN8S7mu19HAgRFxC0Wp8tA63tYwbZZbZuYrI+IAgMy8LyKijiAkSR1RsRo2MxcCCysc4gDgtMz8WETsCpweEdtOjhHwaA2TLB+MiLUoh7yLiC2BB6qcXJLUMc128LkV2LRneV65rtcbgH0AMvOiiJgLbAzcUeXEw1TDHgV8G9g0Ij5PMUjBe6qcXJKkISwGtoqIzSNiTeBVwKIp+/wCeAFARPwZMBf4VdUTD1SyjIjVgA2BlwK7UNw6clhm3lk1AElShzRYsszM5RFxCHAOxW0hp2TmNRHxPmBJZi4C3gmcGBFvp6gJPSgz/+QhIMMaKFlm5kREvCczvwx8s+pJJUkdVT0v9Tl8nk3Rcad33ZE989cCu9V93mHaLL8XEe8CvgT8viewX9cdlCSppTo63N0wyfKVFEXat0xZv0V94UiSWs1kyTYUifIvKZLmD4FPNxGUJEmjZJhk+Vngt8Dx5fKry3WvqDsoSVJLjfBg6FUMkyy3zcxtepbPi4hrp91bkjR+OloNO8x9lpdFxC6TCxHxXGBJ/SFJklors9o0ooYpWT4HuDAiflEubwZcFxFXAZmZ29UenSSpXTpashwmWe7TWBSSJI2wYZ5nedOjPcmGm73g0b50LNz9i3NnO4SR9fDPLpntEEbW1i8+drZDGGnf2bD2+9I1CEuWkiT1YW9YSZJmlhOj20mnimF6w0qSNJYsWUqS6mObpSRJfdhmKUlSHx1tszRZSpLq09Fq2EfVwSci1qw7EEmSRlXfZBkR50fE/J7lnYHFTQYlSWqpiYlq04gapBr2WODbEXE8sAnwIuD1jUYlSWqnER4MvYq+yTIzz4mINwPfBe4EdsjMXzYemSSpfUa4dFhF32QZEf+b4gHPzwe2A86PiHdm5jebDk6S1DJj3Bt2I2DnzLwfuCgivg2cBJgsJUljYZBq2LdNWb4J2LupgCRJLTaugxJExOOBw4FtgLmT6zNzzwbjkiS1UUerYQe5z/LzwE+AzYFjgBvx1hFJ0krkxESlaVQNkiw3ysyTgYcy8weZeTBgqVKSNDYG6eDzUPnz9oj4W+A24HHNhSRJaq2OVsMOkiw/EBHrA+8EPgmsB7y90agkSe00rh18MvMb5ew9wB7NhiNJarVxLVlGxObAocD83v0zc9/mwpIktdIId9KpYpBq2K8DJwNnAd28CpIkzWCQZPmHzDy+8UgkSe03rtWwwCci4ijgO8ADkysz87LGopIktdO4dvABngW8huLeysmrkHivpSRpqjEuWb4c2CIzH2w6GElSu43yKDxVDDKCz9XABg3HIUnSyBqkZLkB8NOIWMyKbZbeOiJJWtEYV8Me1XgUkqRuGNdkmZk/mGl7RFyUmbvWF5IkqbU62ht2kDbLfub230WSpPYapBq2n26WuSVJwxvXathHKyIWAAsA1lxjI9ZYfd2mTiVJGhHZ0WRZRzVsrGxlZi7MzB0zc0cTpSSNiYmsNvUREftExHURsSwijphmn1dExLURcU1EfKGOtzVQyTIingTsTFHlujgzf9mz+TV1BCJJ6oAGByWIiDnACcDewC3A4ohYlJnX9uyzFfDPwG6ZeXdEPKGOc/ctWUbEG4EfAy8F9gcujoiDJ7dn5tV1BCJJUh87A8sy8/pyVLkvAvtN2edNwAmZeTdAZt5Rx4kHKVm+G9ghM+8CiIiNgAuBU+oIQJLUIc22WW4C3NyzfAvw3Cn7PB0gIn4EzAGOzsxvVz3xIMnyLuDenuV7y3WSJK2oYrLs7RxaWpiZC4c4xOrAVsDuwDzggoh4Vmb+pkpcgyTLZcAlEXEmRZvlfsDSiHgHQGYeVyUASVJ3ZFZLlmVinC453gps2rM8r1zX6xbgksx8CLghIv6bInkurhLXIL1hfw58nUfupzwTuAFYt5wkSSo02xt2MbBVRGweEWsCrwIWTdnn6xSlSiJiY4pq2eurvq1Bhrs7pupJJEmqKjOXR8QhwDkU7ZGnZOY1EfE+YElmLiq3vTAirgUeBt492eemir7JMiJ2BN4LPLV3/8zcrurJJUkd0/CgBJl5NnD2lHVH9swn8I5yqs0gbZafp+gRexXQzRFyJUm16OoIPoMky1+VRVtJkmY2xsnyqIg4CTiXFR/+/LXGopIkaYQMkixfDzwDWINHqmETMFlKklbU0ca6QZLlTpm5deORSJJar6ttloPcZ3lhRGzTeCSSpPZr+Kkjs2WQkuUuwBURcQNFm2VQ9M711hFJ0orGuBp2n8ajkCRphPWths3MmyjG4tuznL9vkNdJksZPTmSlaVQNMoLPUcCOwNbAqRS9Yj8H7NZsaJKk1hnjati/A3YALgPIzNsiwgHUJUl/YpRLh1UMkiwfzMyMiASIiHUajkmS1FYdLVkO0vb45Yj4DLBBRLwJ+B5wYrNhSZI0OgYpWT4eOAP4LUW75ZHAXk0GJUlqp+xoyXKQZLl3Zh4OfHdyRUR8DDi8sagkSe00bskyIv4ReAuwRUQs7dm0LvCjpgOTJLXPOJYsvwB8CzgWOKJn/b2Z+etGo5IkaYRMmywz8x7gHuCAVReOJKnVxrBkKUnSUMaxGlaSpKGYLCVJ6qOrydIB0SVJ6mOVlCyfveHmq+I0rfXwzy6Z7RBG1pytnjvbIYyseXM3nu0QRtpFq68x2yGMrD2bPHhGk0efNVbDSpJq09VqWJOlJKk2OdHNkqVtlpIk9WHJUpJUG6thJUnqI+3gI0nSzCxZSpLUhx18JEkaU5YsJUm1yZztCJphspQk1aar1bADJcuI2A04Gnhq+ZoAMjO3aC40SVLbjHWyBE4G3g5cCjzcXDiSpDYb92rYezLzW41GIknSiBo0WZ4XER8BvgY8MLkyMy9rJCpJUiuNezXs5HOSduxZlzT8pBdJUruM9Qg+mblH04FIktpvLEfwiYgDM/NzEfGOlW3PzOOaCUuSpNHRr2S5Tvlz3aYDkSS138Q4VsNm5mfKn8esmnAkSW021m2WEbE5cCgwv/c1mblvM2FJktpo3HvDfp1iYIKzgI4230qSqmp6UIKI2Af4BDAHOCkzPzjNfi8DzgB2yswlVc87aLL8Q2YeX/VkkiQ9WhExBzgB2Bu4BVgcEYsy89op+60LHAZcUte5B02Wn4iIo4Dv4KAEkqRpNFwNuzOwLDOvB4iILwL7AddO2e/9wIeAd9d14kGT5bOA11AMQjBZDeugBJKkFVTtDRsRC4AFPasWZubCcn4T4OaebbfwyKA5k6//c2DTzPxmRKzyZPlyYIvMfLCuE0uSuqdqb9gyMS7su+NKRMRqwHHAQZWCWInVBtzvamCDuk8uSeqWzGpTH7cCm/YszyvXTVoX2BY4PyJuBHYBFkVE71Ctj8qgJcsNgJ9GxGJWbLP01hFJ0qqyGNiqvJ3xVuBVwKsnN2bmPcDGk8sRcT7wrlXZG/aoqieSJHVfkyP4ZObyiDgEOIfi1pFTMvOaiHgfsCQzFzV17kEHUv/BTNsj4qLM3LWekCRJbdX0CD6ZeTZw9pR1R06z7+51nXfQkmU/c2s6jiSpxZoelGC2DNrBp5+OXh5JkuorWUqS1NmnjtRVsvyTqxMRCyJiSUQs+eXvb6vpNJKkUZYZlaZRNXDJMiKeRDHUUAKLM/OXPZtfM3X/3htLd9tkT6tpJWkMjHXJMiLeCPwYeCmwP3BxRBw8uT0zr24mPElSm2TFaVQNWrJ8N7BDZt4FEBEbARcCpzQVmCRJo2LQZHkXcG/P8r3lOkmS/qir1bCDJstlwCURcSZFSXk/YGlEvAMgM49rKD5JUouMciedKgZNlj8vp0lnlj/XrTccSVKbTfTfpZUGHe7umKYDkSS1X/7pnYSdMFCyLB9v8l7gqb2vycztGopLkqSRMWg17OcpesReRXdL2ZKkiiZG+f6PCgZNlr9q8tEnkqRumBjnaljgqIg4CTiXFR/+/LVGopIktdJYt1kCrweeAazBI9WwCZgsJUmdN2iy3Ckzt240EklS63W1U8ugTx25MCK2aTQSSVLrJVFpGlWDlix3Aa6IiBso2iwDSG8dkST16mrJctBkuU+jUUiSOqGryXKgatjMvAnYFNiznL9v0NdKktR2g47gcxSwI7A1cCpFr9jPAbs1F5okqW1Gud2xikGrYf8O2AG4DCAzb4sIB1GXJK1gopu5cuBk+WBmZkQkQESs02BMkqSW6uoIPoO2O345Ij4DbBARbwK+B5zYXFiSpDbKitOoGrRk+XjgDOC3FO2WRwJ7NRWUJEmjZNBkuXdmHg58d3JFRHwMOLyRqCRJrdTVW0dmTJYR8Y/AW4AtImJpz6Z1gR81GZgkqX0mopttlv1Kll8AvgUcCxzRs/7ezPx1Y1FJklpplNsdq5gxWWbmPcA9wAGrJhxJkkbPoG2WkiT1NZZtlpIkDWPcByWQJKmvrg5KYLKUJNWmqx18fHKIJEl9rJKS5b8u32hVnKa1tn7xsbMdwsiaN3fj2Q5hZJ1/5UmzHcJI2/PZb5rtEEbWexs8tm2WkiT1YW9YSZL66GqbpclSklSbrlbD2sFHkqQ+LFlKkmpjm6UkSX10NVlaDStJqk1GtamfiNgnIq6LiGURccRKtr8jIq6NiKURcW5EPLWO92WylCS1QkTMAU4AXgRsAxwQEdtM2e1yYMfM3A44A/hwHec2WUqSajNRcepjZ2BZZl6fmQ8CXwT2690hM8/LzPvKxYuBeVXfE9hmKUmqUcNtlpsAN/cs3wI8d4b93wB8q44TmywlSbWpOihBRCwAFvSsWpiZCx/FcQ4EdgT+qmJIgMlSklSjqoMSlIlxuuR4K7Bpz/K8ct0KImIviiFw/yozH6gWUcE2S0lSWywGtoqIzSNiTeBVwKLeHSJiB+AzwL6ZeUddJ7ZkKUmqTZNtlpm5PCIOAc4B5gCnZOY1EfE+YElmLgI+AjwW+EpEAPwiM/etem6TpSSpNk0PSpCZZwNnT1l3ZM/8Xk2c12QpSapNV586YpulJEl9WLKUJNWmq4/oMllKkmrT1YHUTZaSpNp0tc3SZClJqs1ER9OlHXwkSerDkqUkqTZj22YZEVcxQzV0+cwwSZI6Wgk7WMnyxeXPfyp/nl7+/Pv6w5EktdnYliwz8yaAiNg7M3fo2XRERFwGHNFUcJKkdunqfZbDdPCJiNitZ+Evhny9JEmtNEwHnzcAp0TE+kAAdwMHNxKVJKmVunrryMDJMjMvBZ5dJksy857GopIktVI3U+UQyTIiHgO8DJgPrF4+J4zMfF8jkUmSWmdsO/j0OBO4B7gUeKCZcCRJGj3DJMt5mblPY5FIklqvq22Ww/RmvTAinjXozhGxICKWRMSSs+6//lGEJklqm6w4japhSpZ/CRwUETdQVMMGkNON4JOZC4GFAOc/8eWjfA0kSTWxzRJe1FgUkqRO6Go17CBjw66Xmb8F7l0F8UiSNHIGKVl+gWJ82EspqpR7BzNKYIsG4pIktVA3y5WDjQ07OZD6j4AfAD/MzJ82GpUkqZW62mY5TG/Yk4EnA5+MiOsj4oyIOKyhuCRJLZQV/42qYYa7Oy8iLgB2AvYA3gxsC3yiodgkSS3T1ZLlMMPdnQusA1wE/BDYKTPvaCowSZJGxTDVsEuBBylKk9sB20bEWo1EJUlqpQmy0jSqhqmGfTtARKwLHAScCjwJeEwjkUmSWmd00101w1TDHgI8D3gOcCNwCkV1rCRJwBgPStBjLnAccGlmLm8oHkmSRs4w1bAfbTIQSVL7jX1vWEmS+hnleyWrMFlKkmpjyVKSpD66WrIc5j5LSZLGkiVLSVJtrIaVJKmPiexmNazJUpJUm26mSpOlJKlGXR3Bxw4+kiT1YclSklSbrt46YrKUJNXG3rCSJPVhm6UkSbMsIvaJiOsiYllEHLGS7Y+JiC+V2y+JiPl1nNdkKUmqTVb8N5OImAOcALwI2AY4ICK2mbLbG4C7M/NpwMeBD9XxvkyWkqTaTFSc+tgZWJaZ12fmg8AXgf2m7LMf8Nly/gzgBRERVd4TmCwlSTXKzEpTRCyIiCU904Kew28C3NyzfEu5jpXtk5nLgXuAjaq+Lzv4SJJqU7WDT2YuBBbWE019LFlKktriVmDTnuV55bqV7hMRqwPrA3dVPbHJUpJUm4bbLBcDW0XE5hGxJvAqYNGUfRYBryvn9we+n1l9dPdVUg27190XrorTtNZ3NtxttkMYWRetvsZshzCy9nz2m2Y7hJH2/StPnO0QxlKTI/hk5vKIOAQ4B5gDnJKZ10TE+4AlmbkIOBk4PSKWAb+mSKiV2WYpSapN04MSZObZwNlT1h3ZM/8H4OV1n9dkKUmqTQ01niPJNktJkvqwZClJqo0DqUuS1IeP6JIkqQ+fOiJJ0piyZClJqk1Xe8OaLCVJtelqNazJUpJUGzv4SJLUx0RHq2Ht4CNJUh+WLCVJtelmuXLAkmVEzImI85oORpLUbhNkpWlUDVSyzMyHI2IiItbPzHuaDkqS1E6jnPCqGKYa9nfAVRHxXeD3kysz8621RyVJaiXvs4SvlZMkSWNl4GSZmZ+NiLWAzTLzugZjkiS1VFerYQe+dSQiXgJcAXy7XN4+IhY1FJckqYWy4r9RNcx9lkcDOwO/AcjMK4Atao9IktRamVlpGlXDJMuHVtITtqvP+ZQk6Y+G6eBzTUS8GpgTEVsBbwUubCYsSVIbjX2bJXAo8EzgAeA/gd8Cb2sgJklSS3W1GnaY3rD3Ae8tJ0mS/kRXS5YDJ8uIeDrwLmB+7+syc8/6w5IktdEo92itYpg2y68AnwZOAh5uJhxJkkbPMMlyeWZ+qrFIJEmt19XnWfZNlhHxuHL2rIh4C/BfFJ18AMjMXzcUmySpZca5GvZSikeURbn87p5tiQMTSJJKY1uyzMzNASJibmb+oXdbRMxtKjBJUvt0tWQ5zH2WKxuAwEEJJEmdN0ib5ZOATYC1ImIHHqmOXQ9Ye4bXLQAWAMSc9VlttXWqRytJGmljWw0L/DVwEDAPOK5n/b3Av0z3osxcCCwEWH3NTbp59SRJK+hqNewgbZafBT4bES/LzK+ugpgkSS01ziVLADLzqxHxtxTjw87tWf++JgKTJGlUDDPc3acp2ij3oBjFZ3/gxw3FJUlqoa5Www7TG/YvMvO1wN2ZeQywK/D0ZsKSJLVR5kSlaVQNM9zd/eXP+yLiKcBdwJPrD0mS1FZj/9QR4BsRsQHwEeAyitF7TmoiKElSO43yMymrGKaDz/vL2a9GxDeAuZl5TzNhSZI0OgZus4yItSPif0fEiZn5APCEiHhxg7FJklpmgqw0japhOvicSvG0kV3L5VuBD9QekSSptTKz0jSqhmmz3DIzXxkRBwBk5n0REf1eJEkaH10dlGCYkuWDEbEWRcceImJLep5rKUnSbIqIx0XEdyPiZ+XPDVeyz/YRcVFEXBMRSyPilYMce5hkeRTwbWDTiPg8cC7wniFeL0nquKz4r6IjgHMzcyuKHHXESva5D3htZj4T2Af4t/JOjxkNUw37OuCbwBnA9cBhmXnnEK+XJHXcLLc77gfsXs5/FjgfOLx3h8z875752yLiDuDxwG9mOvAwyfJk4HnA3sCWwOURcUFmfmKIY0iSOmyWe7Q+MTNvL+d/CTxxpp0jYmdgTeDn/Q48zH2W50XEBcBOFOPDvpliUHWTpSQJqF6y7H0Wcmlh+cjHye3fA560kpe+d0ocGRHTBhMRTwZOB16XA4yzN8xA6ucC6wAXAT8EdsrMOwZ9vSRJ/fQ+C3ma7XtNty0i/icinpyZt5fJcKU5KiLWo2hWfG9mXjxIXMN08FkKPAhsC2wHbFv2jpUkCShuHakyVbSIon8N5c8zp+4QEWsC/wX8R2aeMeiBB06Wmfn2zHw+8FKKQdRPpU+DqCRpvMzyoAQfBPaOiJ8Be5XLRMSOETE5lvkrgOcDB0XEFeW0fb8DD1MNewhFB5/nADcCp1BUx0qSBMxuB5/MvAt4wUrWLwHeWM5/DvjcsMcepjfsXOA44NLMXD7siSRJ3TfKQ9ZVMUxv2I82GYgkSaNqmJKlJEkz6urYsCZLSVJtahiybiSZLCVJtelqyXKY+ywlSRpLliwlSbUZ+96wkiT1Y5ulJEl9WLKUJKmPriZLO/hIktSHJUtJUm26Wa6E6GqReSYRsaD3YaJ6hNdmZl6f6XltZub1abdxrYZd0H+XseW1mZnXZ3pem5l5fVpsXJOlJEkDM1lKktTHuCZL2w2m57WZmddnel6bmXl9WmwsO/hIkjSMcS1ZSpI0sE4ly4jYMSKO77PPUyLijHJ++4j4mwGOu8J+EbFvRBxRPeLBRcQGEfGWVXlOqZ+IODsiNhhi//kRcXWDIY2kiDgoIp7Ss3xjRGw8mzFpOCOdLCNizjD7Z+aSzHxrn31uy8z9y8Xtgb7Jcup+mbkoMz84TGw12AAwWc5g1L9QRMSbI+K1sx1HnTLzbzLzN7MdRwscBDyl306DiAgHk5kNmTkrEzAf+CnweeAnwBnA2sCNwIeAy4BXAS8ELiqXvwI8tnz9TsCFwJXAj4F1gd2Bb5TbjwZOL1/7M+BNPee9GlgT+AXwK+AK4JXAzuX+l5fH3nqa/Q4C/m/P8b4PLAXOBTYr158GHF8e53pg/4rX64vA/WUMHwHeDSwuz3vMlGt6GvDf5bXdC/hReQ12nunaTHPe3YEfAGeW7+ODwN+X1/wqYMtyv5cAl5TX7nvAE8v1nwCOLOf/GrgAWK3B36mrZ+t3uotT+Xv21nL+48D3y/k9y9+vG4GNy2v/E+BE4BrgO8Ba5b7Pofg7vbL83R35/6MB3vd0n0tHln+XV1N06Algf+B3wHXl3+9a5XU7pnz9VcAzytevA5xS/n1dDuxXrj8IWETxWfOD2b4+4zjN5i/jfIqRkXYrl08B3lX+Er2nXLdx+eG6Trl8ePnLuGb5wb1TuX49iqH7dmfFZHll+Yu5MXAzxTe7P36g0pP0eo9Tzu8FfHWa/f64DJwFvK6cPxj4ejl/WvlHtBqwDbCshus1GfcLe/4QVwO+ATy/3Gc58Kxy/aXldQ1gv57YVnptpjnv7sBvgCcDjwFu5ZHkfBjwb+X8hjzSYeyNwMfK+bUpPjz3oPiw2LLB36lR/0JxNPCucv58ii+FPy7jeN5s/S32uaa7AF8p539YxrsGcBTwD6yYLJcD25f7fhk4sJxfCjy/nG9LspzpfR/OSj6XyvnH9RzjdOAlPf/fO/ZsuxE4tJx/C3BSOf9/eq7bBuXvxjoUnzm39B7fadVOs10Ne3Nm/qic/xzwl+X8l8qfu1Akmh9FxBXA64CnUpT4bs/MxQCZ+dvMXL6S45+Zmfdn5p3AeRQlx5msD3ylbFP5OPDMAd7DrsAXyvnTe94DFMlpIjOvBZ44wLEG9cJyupzim+kzgK3KbTdk5lWZOUGRpM7N4i/vKooPtEnDXJvFmXl7Zj4A/Jyi1MCUY84DzomIqyiS1DMBMvM+4E3Adym+YPz8Ub/r/o4Afp6Z25fn24rifW0PPCcinl/u9zTgYxTX7RnAqyn+394F/EvP8bajKEnsChzZ2+a0Es8G3gz8GfAa4OmZuTNwEnDoNK9ZvdznbRQfwqPoUoprtx7wAMWXhx2B51EkkV43ZOYVPa+bX7ZnbpCZF5TrT2884nrM9L7vZ+WfSwB7RMQl5d/Bnsz8GfK1nnPNL+dfCBxRHvd8YC6wWbntu5n566pvTI/ObNd9T71vZXL59+XPoPgFOaB3p4h4VsXjT+f9wHmZ+XcRMZ/il7WKB3rmo+KxegVwbGZ+ZoWVRcy955zoWZ5gxf/vYa7NIMf8JHBcZi6KiN0pSlGTngXcRU1tNgPq/UIB8FiK5PkLyi8UABHxxy8U5Qfc/J5jnJmZ9wP3R8TkF4qvT3O+xZl5e3nMqV8o9pjmNSv7sBwpmflQRNxAUbK5kKKUuAfFF46fTNm99/fkYYqai1bq875vYOWfS3OBf6coQd4cEUdTJLvpTF6vh3nk7yiAl2XmdVOO/Vwe+VzULJjtkuVmEbFrOf9q4P9N2X4xsFtEPA0gItaJiKdTVOc9OSJ2KtevO02j934RMTciNqKoKls8Zfu9FG2dk9anqGaE4o9kuv16XUjRtgpF1dvUb9t16Y3hHODgiHgsQERsEhFPGPJ4/a7NsHqv3esmV0bEU4F3AjsALyr/6FeFyS8U25fT0zLz5HLbbH2hmO41D8+wzyj4IUWp+4Jy/s3A5WWNxYyy6Pzzm4iYrHH5+6aCbMBK3zfTfy5NJsY7y7/N/XuONdNnSK9zgEMjIspj71DHG1F1s50srwP+KSJ+QtHm9anejZn5K4qk9Z8RsZSiKuQZmfkgRUebT0bElRRVbiv7BreUoorxYuD9mXnblO3nAdtExBUR8Urgw8CxEXE5K354Td2v16HA68v4XkPRjle7zLyLotrnamBviqrfi8rS0BkM9ofYq9+1GdbRFFXYlwJ3ApR/8CdTtNPdBrwBOKn8Bt6EUf9C0VY/pGizvigz/wf4A8N9KXw9cEJZtVhnDUvTVvq+Z/hc+g1FB6erKX7/en9fTgM+XX6GzFTifj9F2+jSstbj/bW+Iz1qszaCT1ll+I3M3Lah4x8N/C4zP9rE8dusy9cmIr5A0db4LYoOEW8sN/0OOJCiFPfH37uIOK1cPqP3d7K8RltQVN1uDHw4M0+c5py7U3wheHG5fH65vKR3W+91n7LPxsCSzJxf24WQVCuT5Rjy2vTnNZLUy7FhBfyx09TUnooPZOaqamMcKSZLSb1MltKA/EIhjS+TpSRJfcx2b1hJkkaeyVKSpD5MlpIk9WGylCSpD5OlJEl9/H98nIBwQyAZ+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes=plt.subplots(figsize=(8, 6))\n",
    "sns.heatmap(df.corr(), ax=axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54b4bc5",
   "metadata": {
    "papermill": {
     "duration": 0.019975,
     "end_time": "2022-01-17T18:28:25.760945",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.740970",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can observe that wind and preception are weakly co-related.temp_max and wind are negatively correlated means they move in the opposite direction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6199b721",
   "metadata": {
    "papermill": {
     "duration": 0.020226,
     "end_time": "2022-01-17T18:28:25.801083",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.780857",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Machine Learning Model for Prediction of Weather..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e03df9e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:25.849813Z",
     "iopub.status.busy": "2022-01-17T18:28:25.849110Z",
     "iopub.status.idle": "2022-01-17T18:28:25.909728Z",
     "shell.execute_reply": "2022-01-17T18:28:25.910319Z",
     "shell.execute_reply.started": "2022-01-17T18:26:30.871953Z"
    },
    "papermill": {
     "duration": 0.087338,
     "end_time": "2022-01-17T18:28:25.910505",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.823167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "features=[\"precipitation\", \"temp_max\", \"temp_min\", \"wind\"]\n",
    "X=df[features]\n",
    "y=df.weather\n",
    "train_X, test_X, train_y, test_y = train_test_split(X, y,random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3412f643",
   "metadata": {
    "papermill": {
     "duration": 0.020598,
     "end_time": "2022-01-17T18:28:25.952976",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.932378",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Decision Tree Regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6ada6af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:25.999213Z",
     "iopub.status.busy": "2022-01-17T18:28:25.998361Z",
     "iopub.status.idle": "2022-01-17T18:28:26.183378Z",
     "shell.execute_reply": "2022-01-17T18:28:26.183960Z",
     "shell.execute_reply.started": "2022-01-17T18:27:52.558428Z"
    },
    "papermill": {
     "duration": 0.211343,
     "end_time": "2022-01-17T18:28:26.184155",
     "exception": false,
     "start_time": "2022-01-17T18:28:25.972812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.713115\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "model1=DecisionTreeRegressor(random_state=1)\n",
    "model1.fit(train_X, train_y)\n",
    "pred1=model1.predict(test_X)\n",
    "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdfc40b",
   "metadata": {
    "papermill": {
     "duration": 0.020228,
     "end_time": "2022-01-17T18:28:26.224737",
     "exception": false,
     "start_time": "2022-01-17T18:28:26.204509",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Random Forest Regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8762a4b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:26.271563Z",
     "iopub.status.busy": "2022-01-17T18:28:26.269644Z",
     "iopub.status.idle": "2022-01-17T18:28:26.603478Z",
     "shell.execute_reply": "2022-01-17T18:28:26.602952Z",
     "shell.execute_reply.started": "2022-01-17T18:27:54.071643Z"
    },
    "papermill": {
     "duration": 0.357538,
     "end_time": "2022-01-17T18:28:26.603623",
     "exception": false,
     "start_time": "2022-01-17T18:28:26.246085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.694026\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model2=RandomForestRegressor(random_state=1)\n",
    "model2.fit(train_X, train_y)\n",
    "pred2=model2.predict(test_X)\n",
    "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred2)))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491907bf",
   "metadata": {
    "papermill": {
     "duration": 0.020355,
     "end_time": "2022-01-17T18:28:26.644903",
     "exception": false,
     "start_time": "2022-01-17T18:28:26.624548",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Extreme Gradient Regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b4aa12e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T18:28:26.693123Z",
     "iopub.status.busy": "2022-01-17T18:28:26.692193Z",
     "iopub.status.idle": "2022-01-17T18:28:27.094277Z",
     "shell.execute_reply": "2022-01-17T18:28:27.094978Z",
     "shell.execute_reply.started": "2022-01-17T18:26:36.390565Z"
    },
    "papermill": {
     "duration": 0.429633,
     "end_time": "2022-01-17T18:28:27.095173",
     "exception": false,
     "start_time": "2022-01-17T18:28:26.665540",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.682803\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "model3= XGBRegressor(n_estimators=100, learning_rate=0.04)\n",
    "model3.fit(train_X, train_y)\n",
    "pred3=model3.predict(test_X)\n",
    "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3854c2ff",
   "metadata": {
    "papermill": {
     "duration": 0.020554,
     "end_time": "2022-01-17T18:28:27.138278",
     "exception": false,
     "start_time": "2022-01-17T18:28:27.117724",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The least mean absolute error we got is of the XGB Regressor Model. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 15.101768,
   "end_time": "2022-01-17T18:28:27.871289",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-01-17T18:28:12.769521",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}