bike_sharing_analysis_part_5_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f51e8fa-f0ea-41db-a34d-97af2f7cc885",
   "metadata": {},
   "source": [
    "# Machine Learning exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82659f58-8342-4249-a27c-e960a65833cd",
   "metadata": {},
   "source": [
    "#### The objective is to predict the missing values for the \"registered\" and \"casual\" users from the original dataset (null values). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad017566-efe0-40fc-bbc8-d21cce914bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.dummy import DummyClassifier, DummyRegressor\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f088082-9ec9-45e0-97a8-9a5bb85600e3",
   "metadata": {},
   "source": [
    "## Step 0: Explore orginal dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97c6bfed-bc06-4b47-aad2-d50c9b90ddb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379, 11)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_bikes= pd.read_csv('data_sets/dc_bikes.csv', parse_dates=True, index_col=0)\n",
    "dc_bikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4781ac6-506f-4cde-bb3d-42a1f0702e4b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
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       "      <th>datetime</th>\n",
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      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 05:00:00       1        0           0        2   9.84  12.880   \n",
       "2011-01-01 06:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 07:00:00       1        0           0        1   8.20  12.880   \n",
       "2011-01-01 08:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 09:00:00       1        0           0        1  13.12  17.425   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  \n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0     16  \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0     40  \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0     32  \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0     13  \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0      1  \n",
       "2011-01-01 05:00:00        75     6.0032     0.0         1.0      1  \n",
       "2011-01-01 06:00:00        80     0.0000     2.0         0.0      2  \n",
       "2011-01-01 07:00:00        86     0.0000     1.0         2.0      3  \n",
       "2011-01-01 08:00:00        75     0.0000     1.0         7.0      8  \n",
       "2011-01-01 09:00:00        76     0.0000     8.0         6.0     14  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_bikes.head (10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96869f4d-57f9-4a1c-9417-3d8a7d8ed938",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_bikes['hour']=dc_bikes.index.hour\n",
    "dc_bikes['month']=dc_bikes.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4783436-abfd-4dc4-8e70-bab4fa154b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season           0\n",
       "holiday          0\n",
       "workingday       0\n",
       "weather          0\n",
       "temp             0\n",
       "atemp            0\n",
       "humidity         0\n",
       "windspeed        0\n",
       "casual        6493\n",
       "registered    6493\n",
       "count            0\n",
       "hour             0\n",
       "month            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_bikes.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d87495-da73-4521-8a8f-485b9c2b62e5",
   "metadata": {},
   "source": [
    "#### This null values for \"registered\" and \"casual\" are the ones that will be predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc78ba63-464e-43d4-8595-6b7a7eb27515",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Step 1: Predict registered users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a6fa05-8c55-4c40-9867-1ce5fa676167",
   "metadata": {},
   "source": [
    "### a. Train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a527ad91-ea66-4f61-966f-b8b48bfb34b2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. use train.csv\n",
    "\n",
    "df_train = pd.read_csv('data_sets/train.csv', index_col = 0, parse_dates=True)\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43a451d5-4e00-47c2-b8e1-f798991ae83b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.index.hour\n",
    "df_train['hour_day'] = df_train.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d52ae27-d96e-42d0-9978-0439da95c847",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#2. drop \"count\" and \"casual\" columns\n",
    "\n",
    "df_train.drop(columns=['count','casual'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a7f5af8-8422-4053-893e-60010836abec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74021a72-28cc-4216-ad45-33fa23490d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. define X as the features and y as \"registered\"\n",
    "\n",
    "X = df_train[df_train.columns[df_train.columns!='registered']]\n",
    "y = df_train['registered']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9269c3c3-698d-43d5-a327-2b9354e566a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4. split into train and test data to find the best fit model and improve features->\n",
    "   # Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4011e275-bbe1-4a15-98ae-6c837d534d0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7620, 9)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b60dd1d9-fab2-4b58-a22a-03db26aa3a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3266, 9)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ddb8aea2-2efb-409c-9363-50c2b01a281c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5. use X_train to train a model to predict registered user and find the fit model\n",
    "\n",
    "m = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09572d30-c48f-47e8-a4bf-4b1434d3304d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "21478021-3627-419f-89c7-6dee19810699",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_registered_train=m.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a77bf07-bc1a-4f18-885a-d936be44fc1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3266,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_registered_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8906308a-fd90-45bd-8e07-9efaa98185a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127.97141229318626"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_registered_train, ytest)**0.5 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80536b8d-df4a-46e2-8bb2-df50a5f425a0",
   "metadata": {},
   "source": [
    "### Evaluate model (here on test dataset), first we do only prediction for y_registered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5243a7ab-ac62-4127-b3b2-21c8434dcacb",
   "metadata": {
    "tags": []
   },
   "source": [
    "### b. Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8bc86116-51a3-4ac2-99eb-8c4ee608d1ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('data_sets/test.csv', index_col = 0, parse_dates=True)\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f210377-b3cd-427d-9123-31d7132679bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.index.hour\n",
    "df_test['hour_day'] = df_test.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc7fd080-fead-4c10-9fb8-e854991bdc07",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-20 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "                     humidity  windspeed  hour_day  \n",
       "datetime                                            \n",
       "2011-01-20 00:00:00        56    26.0027         0  \n",
       "2011-01-20 01:00:00        56     0.0000         1  \n",
       "2011-01-20 02:00:00        56     0.0000         2  \n",
       "2011-01-20 03:00:00        56    11.0014         3  \n",
       "2011-01-20 04:00:00        56    11.0014         4  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60af40a4-5f63-4f0c-9a87-c7889936ef86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd6c48cd-9678-4426-9966-37eda96f4293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest2=df_test[df_test.columns]\n",
    "Xtest2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24db15f1-ee82-4046-988b-72869df96283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 28.44813254,  34.02469824,  40.62870195, ..., 161.49619864,\n",
       "       175.9966243 , 169.52803232])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_registered_pred=m.predict(Xtest2)\n",
    "y_registered_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badaa46b-96b5-4ca1-8444-d9c624d24723",
   "metadata": {},
   "source": [
    "### Registered users (prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72ac995f-cbfc-4d82-bba5-e5f08f08cb07",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "    <tr>\n",
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       "               0\n",
       "0      28.448133\n",
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       "4      55.038178\n",
       "...          ...\n",
       "6488  144.465601\n",
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       "6491  175.996624\n",
       "6492  169.528032\n",
       "\n",
       "[6493 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_registered_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9f10a9-9aac-49d5-be53-2fec4419bafb",
   "metadata": {},
   "source": [
    "## Step 2 predict casual users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336e7924-90f6-49c0-a5c0-10f0611ef80f",
   "metadata": {},
   "source": [
    "### a. Train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5436e386-2117-40ab-b52a-f9f8662c6480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10886, 11)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. use train.csv\n",
    "\n",
    "df_train2 = pd.read_csv('data_sets/train.csv', index_col = 0, parse_dates=True)\n",
    "df_train2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb986ac3-3160-46c0-a5e7-55c20162cb29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train2.index.hour\n",
    "df_train2['hour_day'] = df_train2.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f8469533-70af-4bad-89b0-9c914d365f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. drop \"registered\" and \"count\" columns\n",
    "\n",
    "df_train2.drop(columns=['registered', 'count'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b01e600f-bf5f-453e-9819-dde8179f4874",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. define X as the features and y as \"casual\"\n",
    "\n",
    "X2 = df_train2[df_train2.columns[df_train2.columns!='casual']]\n",
    "y2 = df_train2['casual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4e0ddcdd-c0a5-4fdd-b8a8-3ff32769954d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4. split into train and test data to find the best fit model and improve features->\n",
    "   # Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "\n",
    "X2train, X2test, y2train, y2test = train_test_split(X2, y2, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1fd035c2-3a73-4b9f-9b58-061d552380db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7620, 9)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0f83fed2-dd8d-4290-89cb-2fe63aa940c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3266, 9)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddae174a-0402-4579-9b3f-d825f50d48c9",
   "metadata": {},
   "source": [
    "### Find the fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b7b32bb4-c72e-42ef-99f6-965bebc94462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.fit(X2train, y2train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9d1862e5-f8d2-4764-94a7-adb5b63dd2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_casual_train=m.predict(X2test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "405874fb-b438-4555-b90c-e895c3a97bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.712563358600654"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_casual_train, y2test)**0.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9f1f8783-59ce-4606-942f-acb892b84169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test2 = pd.read_csv('data_sets/test.csv', index_col = 0, parse_dates=True)\n",
    "df_test2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bae699-08fb-4a41-a01b-c58692048273",
   "metadata": {},
   "source": [
    "### b. Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63899eac-6ad4-418c-9fed-baa2a56b72aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test2 = pd.read_csv('data_sets/test.csv', index_col = 0, parse_dates=True)\n",
    "df_test2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c7f41a5d-dd63-4a3b-9f7b-7a368649e0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test2.index.hour\n",
    "df_test2['hour_day'] = df_test2.index.hour\n",
    "df_test2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ee5f0a44-263a-4b10-b0b8-646ace14eb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 9)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2test2=df_test2[df_test2.columns]\n",
    "X2test2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1df09f1-7ad6-4959-b51f-fdfb516cba03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-17.85663778, -12.62867251, -11.45787825, ...,   6.09026052,\n",
       "        11.59344044,   5.44676273])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_casual_pred=m.predict(X2test2)\n",
    "y_casual_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed12f6e-f18f-40c9-8bf7-e2a146131739",
   "metadata": {},
   "source": [
    "### Casual users (prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ce22cc6-b8a1-4c60-a759-c7bba44e58e5",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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      "text/plain": [
       "              0\n",
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       "...         ...\n",
       "6488   6.709532\n",
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       "6491  11.593440\n",
       "6492   5.446763\n",
       "\n",
       "[6493 rows x 1 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_casual_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1123e374-cdb2-4dfa-9422-a179e6c2b612",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Total users (prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cf46e8df-3193-466c-84a9-714929a1c34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6493"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_total_pred = y_registered_pred + y_casual_pred\n",
    "len(y_total_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "77b2b637-1f4f-44e7-b705-f6174ce84cd4",
   "metadata": {},
   "outputs": [
    {
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       "<p>6493 rows × 1 columns</p>\n",
       "</div>"
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      "text/plain": [
       "               0\n",
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       "...          ...\n",
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       "\n",
       "[6493 rows x 1 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_total_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0685a52c-4c1a-42f7-b057-a1932a840be2",
   "metadata": {},
   "source": [
    "# New Dataframe including predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dd62325d-aff8-4f74-b8f8-34e688255595",
   "metadata": {},
   "outputs": [],
   "source": [
    "updated_df = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b15a3f19-0f58-47b1-a1cf-41a651b15825",
   "metadata": {},
   "outputs": [],
   "source": [
    "updated_df['y_registered_pred'] = y_registered_pred\n",
    "updated_df['y_casual_pred'] = y_casual_pred\n",
    "updated_df['y_total_pred'] = y_total_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b081f032-8b14-4546-a95e-08cf87d3f881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 12)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "36cc9bf1-94a6-4f8d-afcc-12d29dd4d9cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
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       "2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "                     humidity  windspeed  hour_day  y_registered_pred  \\\n",
       "datetime                                                                \n",
       "2011-01-20 00:00:00        56    26.0027         0          28.448133   \n",
       "2011-01-20 01:00:00        56     0.0000         1          34.024698   \n",
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       "2011-01-20 04:00:00        56    11.0014         4          55.038178   \n",
       "\n",
       "                     y_casual_pred  y_total_pred  \n",
       "datetime                                          \n",
       "2011-01-20 00:00:00     -17.856638     10.591495  \n",
       "2011-01-20 01:00:00     -12.628673     21.396026  \n",
       "2011-01-20 02:00:00     -11.457878     29.170824  \n",
       "2011-01-20 03:00:00     -11.731826     36.702348  \n",
       "2011-01-20 04:00:00     -10.561032     44.477146  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_df.head()"
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  },
  {
   "cell_type": "markdown",
   "id": "73a79f41-d068-4a17-a270-66b0b4c9b458",
   "metadata": {},
   "source": [
    "# Now, let's compare the predicted values to the ones in the original dataset (dc_bikes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "89a17f12-deab-4008-8dcc-7c0bcabc0576",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
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       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  hour  \\\n",
       "datetime                                                                    \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0     16     0   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0     40     1   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0     32     2   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0     13     3   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0      1     4   \n",
       "\n",
       "                     month  \n",
       "datetime                    \n",
       "2011-01-01 00:00:00      1  \n",
       "2011-01-01 01:00:00      1  \n",
       "2011-01-01 02:00:00      1  \n",
       "2011-01-01 03:00:00      1  \n",
       "2011-01-01 04:00:00      1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f958da24-28b1-4eb9-b693-3a17f98bae9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379, 13)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_bikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e333611b-daaf-47cd-a836-492b327e18b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_bikes_empty=dc_bikes[dc_bikes['casual'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "998d328b-714d-4f22-a01d-490e0f3028a5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>65</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6493 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2012-12-31 19:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 20:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 21:00:00       1        0           1        1  10.66  12.880   \n",
       "2012-12-31 22:00:00       1        0           1        1  10.66  13.635   \n",
       "2012-12-31 23:00:00       1        0           1        1  10.66  13.635   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  hour  \\\n",
       "datetime                                                                    \n",
       "2011-01-20 00:00:00        56    26.0027     NaN         NaN     33     0   \n",
       "2011-01-20 01:00:00        56     0.0000     NaN         NaN     15     1   \n",
       "2011-01-20 02:00:00        56     0.0000     NaN         NaN      9     2   \n",
       "2011-01-20 03:00:00        56    11.0014     NaN         NaN      8     3   \n",
       "2011-01-20 04:00:00        56    11.0014     NaN         NaN      7     4   \n",
       "...                       ...        ...     ...         ...    ...   ...   \n",
       "2012-12-31 19:00:00        60    11.0014     NaN         NaN    156    19   \n",
       "2012-12-31 20:00:00        60    11.0014     NaN         NaN    104    20   \n",
       "2012-12-31 21:00:00        60    11.0014     NaN         NaN     67    21   \n",
       "2012-12-31 22:00:00        56     8.9981     NaN         NaN     43    22   \n",
       "2012-12-31 23:00:00        65     8.9981     NaN         NaN     28    23   \n",
       "\n",
       "                     month  \n",
       "datetime                    \n",
       "2011-01-20 00:00:00      1  \n",
       "2011-01-20 01:00:00      1  \n",
       "2011-01-20 02:00:00      1  \n",
       "2011-01-20 03:00:00      1  \n",
       "2011-01-20 04:00:00      1  \n",
       "...                    ...  \n",
       "2012-12-31 19:00:00     12  \n",
       "2012-12-31 20:00:00     12  \n",
       "2012-12-31 21:00:00     12  \n",
       "2012-12-31 22:00:00     12  \n",
       "2012-12-31 23:00:00     12  \n",
       "\n",
       "[6493 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_bikes_empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bab1e9ac-ca9f-4c2d-b485-21ca09ea2d1b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>104</td>\n",
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       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>67</td>\n",
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       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>43</td>\n",
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       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>28</td>\n",
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       "                     count\n",
       "datetime                  \n",
       "2011-01-20 00:00:00     33\n",
       "2011-01-20 01:00:00     15\n",
       "2011-01-20 02:00:00      9\n",
       "2011-01-20 03:00:00      8\n",
       "2011-01-20 04:00:00      7\n",
       "...                    ...\n",
       "2012-12-31 19:00:00    156\n",
       "2012-12-31 20:00:00    104\n",
       "2012-12-31 21:00:00     67\n",
       "2012-12-31 22:00:00     43\n",
       "2012-12-31 23:00:00     28\n",
       "\n",
       "[6493 rows x 1 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_true= pd.DataFrame(dc_bikes_empty['count'])\n",
    "y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5cfe876b-c988-487c-8244-cd810b4d8dc9",
   "metadata": {},
   "outputs": [
    {
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       "133.15455440533563"
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     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "metrics.mean_squared_error(y_true, y_total_pred)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "393bc63f-4014-4127-a5eb-7d75c5f740d7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>6489</th>\n",
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       "               0\n",
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       "4      44.477146\n",
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       "6492  174.974795\n",
       "\n",
       "[6493 rows x 1 columns]"
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     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(y_total_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "40512483-2c56-4433-98dc-7b44986cacb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes_pred=pd.DataFrame(columns=['count_true', 'count_total_prediction'])\n",
    "bikes_pred['count_true']=y_true\n",
    "bikes_pred['count_total_prediction']=y_total_pred\n",
    "bikes_pred['count_total_prediction']=bikes_pred.count_total_prediction.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "218f4b52-b2e6-4c67-8442-0948d9c758fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8</td>\n",
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       "      <td>156</td>\n",
       "      <td>151</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>104</td>\n",
       "      <td>158</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>167</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>43</td>\n",
       "      <td>187</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>28</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6493 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count_true  count_total_prediction\n",
       "datetime                                               \n",
       "2011-01-20 00:00:00          33                      10\n",
       "2011-01-20 01:00:00          15                      21\n",
       "2011-01-20 02:00:00           9                      29\n",
       "2011-01-20 03:00:00           8                      36\n",
       "2011-01-20 04:00:00           7                      44\n",
       "...                         ...                     ...\n",
       "2012-12-31 19:00:00         156                     151\n",
       "2012-12-31 20:00:00         104                     158\n",
       "2012-12-31 21:00:00          67                     167\n",
       "2012-12-31 22:00:00          43                     187\n",
       "2012-12-31 23:00:00          28                     174\n",
       "\n",
       "[6493 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6627fe6-8a3d-43ee-ade6-958867255c75",
   "metadata": {},
   "source": [
    "### Conclusion: The average squared difference (mean_squared_error) between the estimated values and true values are 133 units. "
   ]
  }
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