{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_20744\\3250454216.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" df['hour'] = pd.to_datetime(df['time']).dt.hour\n"
]
}
],
"source": [
"df= pd.read_csv('data.csv')\n",
"df['hour'] = pd.to_datetime(df['time']).dt.hour\n",
"df['weekday'] = pd.to_datetime(df['date']).dt.weekday\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>address</th>\n",
" <th>car_num</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>time</th>\n",
" <th>date</th>\n",
" <th>hour</th>\n",
" <th>weekday</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372527</td>\n",
" <td>22.580441</td>\n",
" <td>18:48:39</td>\n",
" <td>2023-05-02</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372527</td>\n",
" <td>22.580441</td>\n",
" <td>18:48:39</td>\n",
" <td>2023-05-02</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372527</td>\n",
" <td>22.580441</td>\n",
" <td>18:50:15</td>\n",
" <td>2023-05-02</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372526</td>\n",
" <td>22.580458</td>\n",
" <td>19:16:08</td>\n",
" <td>2023-05-02</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>15, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372552</td>\n",
" <td>22.580432</td>\n",
" <td>19:17:15</td>\n",
" <td>2023-05-02</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" address car_num lat \n",
"0 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \\\n",
"1 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
"2 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
"3 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372526 \n",
"4 15, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372552 \n",
"\n",
" long time date hour weekday \n",
"0 22.580441 18:48:39 2023-05-02 18 1 \n",
"1 22.580441 18:48:39 2023-05-02 18 1 \n",
"2 22.580441 18:50:15 2023-05-02 18 1 \n",
"3 22.580458 19:16:08 2023-05-02 19 1 \n",
"4 22.580432 19:17:15 2023-05-02 19 1 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# df.drop(['time','date','address','car_num'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>address</th>\n",
" <th>car_num</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>time</th>\n",
" <th>date</th>\n",
" <th>hour</th>\n",
" <th>weekday</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372527</td>\n",
" <td>22.580441</td>\n",
" <td>18:48:39</td>\n",
" <td>2023-05-02</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372527</td>\n",
" <td>22.580441</td>\n",
" <td>18:48:39</td>\n",
" <td>2023-05-02</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372527</td>\n",
" <td>22.580441</td>\n",
" <td>18:50:15</td>\n",
" <td>2023-05-02</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372526</td>\n",
" <td>22.580458</td>\n",
" <td>19:16:08</td>\n",
" <td>2023-05-02</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>15, Vidyasagar St, Machuabazar, Kolkata, West ...</td>\n",
" <td>jj</td>\n",
" <td>88.372552</td>\n",
" <td>22.580432</td>\n",
" <td>19:17:15</td>\n",
" <td>2023-05-02</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" address car_num lat \n",
"0 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \\\n",
"1 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
"2 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372527 \n",
"3 40, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372526 \n",
"4 15, Vidyasagar St, Machuabazar, Kolkata, West ... jj 88.372552 \n",
"\n",
" long time date hour weekday \n",
"0 22.580441 18:48:39 2023-05-02 18 1 \n",
"1 22.580441 18:48:39 2023-05-02 18 1 \n",
"2 22.580441 18:50:15 2023-05-02 18 1 \n",
"3 22.580458 19:16:08 2023-05-02 19 1 \n",
"4 22.580432 19:17:15 2023-05-02 19 1 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"x=df[['lat','long','weekday']]\n",
"y= df['hour']"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# adress_map={}\n",
"# temp=[]\n",
"# count=0;\n",
"# for i in x['address']:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div>"
],
"text/plain": [
"RandomForestRegressor(random_state=0)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
" \n",
"# create regressor object\n",
"regressor = RandomForestRegressor(n_estimators=100, random_state=0)\n",
"\n",
"\n",
" \n",
"# fit the regressor with x and y data\n",
"regressor.fit(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18.08 18.08 18.08 18.64 18.97 18.97 18.97 19. 19. 19. 19. 19.\n",
" 19. 19. 19. 19. 19. 19. 19. 19.89 19.89 16.99]\n",
"0 18\n",
"1 18\n",
"2 18\n",
"3 19\n",
"4 19\n",
"5 19\n",
"6 19\n",
"7 19\n",
"8 19\n",
"9 19\n",
"10 19\n",
"11 19\n",
"12 19\n",
"13 19\n",
"14 19\n",
"15 19\n",
"16 19\n",
"17 19\n",
"18 19\n",
"19 20\n",
"20 20\n",
"21 16\n",
"Name: hour, dtype: int32\n"
]
}
],
"source": [
"print(regressor.predict(x))\n",
"print(y)\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"adress= df['address']\n",
"lat= df['lat']\n",
"long= df['long']\n",
"weekday= df['weekday']\n",
"\n",
"a1=[]\n",
"lat1=[]\n",
"long1=[]\n",
"weekday1=[]\n",
"\n",
"\n",
"\n",
"i =0\n",
"\n",
"while i<len(adress):\n",
" if adress[i] == '40, Vidyasagar St, Machuabazar, Kolkata, West Bengal 700009, India':\n",
" a1.append(adress[i])\n",
" lat1.append(lat[i])\n",
" long1.append(long[i])\n",
" weekday1.append(weekday[i])\n",
" break\n",
" i=i+1\n",
"\n",
"df2= pd.DataFrame({'lat':lat1,'long':long1,'weekday':weekday1})\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18.08]\n"
]
}
],
"source": [
"print(regressor.predict(df2))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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