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2022-020
Commit 47673403 authored by W.P.S.M wickramage's avatar W.P.S.M wickramage
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('BusTravelData.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\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>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1/1/2019</td>\n",
" <td>6</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>25</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1/2/2019</td>\n",
" <td>7</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>27</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1/3/2019</td>\n",
" <td>8</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>28</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1/4/2019</td>\n",
" <td>9</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1/5/2019</td>\n",
" <td>10</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops Weather \\\n",
"0 1/1/2019 6 Sunday No 5 40 14 25 \n",
"1 1/2/2019 7 Sunday No 8 40 14 27 \n",
"2 1/3/2019 8 Sunday No 9 40 14 28 \n",
"3 1/4/2019 9 Sunday No 7 40 14 29 \n",
"4 1/5/2019 10 Sunday No 6 40 14 29 \n",
"\n",
" Distance Travel Time \n",
"0 14.7 56 \n",
"1 14.7 55 \n",
"2 14.7 55 \n",
"3 14.7 56 \n",
"4 14.7 56 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>595</th>\n",
" <td>8/18/2020</td>\n",
" <td>6</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>596</th>\n",
" <td>8/19/2020</td>\n",
" <td>6</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>597</th>\n",
" <td>8/20/2020</td>\n",
" <td>12</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>598</th>\n",
" <td>8/21/2020</td>\n",
" <td>12</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>8/22/2020</td>\n",
" <td>12</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops \\\n",
"595 8/18/2020 6 Monday No 9 40 12 \n",
"596 8/19/2020 6 Monday No 3 40 12 \n",
"597 8/20/2020 12 Monday No 5 40 12 \n",
"598 8/21/2020 12 Monday No 9 40 12 \n",
"599 8/22/2020 12 Monday No 3 40 12 \n",
"\n",
" Weather Distance Travel Time \n",
"595 25 12.1 49 \n",
"596 25 12.1 40 \n",
"597 25 12.1 43 \n",
"598 25 12.1 49 \n",
"599 25 12.1 40 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(600, 10)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows 600\n",
"Number of columns 10\n"
]
}
],
"source": [
"print(\"Number of rows\",data.shape[0])\n",
"print(\"Number of columns\",data.shape[1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 600 entries, 0 to 599\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Date 600 non-null object \n",
" 1 Time 600 non-null int64 \n",
" 2 Day 600 non-null object \n",
" 3 Special 600 non-null object \n",
" 4 Congestion 600 non-null int64 \n",
" 5 Drving Speed 600 non-null int64 \n",
" 6 Stops 600 non-null int64 \n",
" 7 Weather 600 non-null int64 \n",
" 8 Distance 600 non-null float64\n",
" 9 Travel Time 600 non-null int64 \n",
"dtypes: float64(1), int64(6), object(3)\n",
"memory usage: 47.0+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
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"\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>Time</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>600.000000</td>\n",
" <td>600.000000</td>\n",
" <td>600.0</td>\n",
" <td>600.000000</td>\n",
" <td>600.000000</td>\n",
" <td>600.00000</td>\n",
" <td>600.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>12.546667</td>\n",
" <td>6.253333</td>\n",
" <td>40.0</td>\n",
" <td>12.000000</td>\n",
" <td>27.000000</td>\n",
" <td>12.40400</td>\n",
" <td>49.786667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.184524</td>\n",
" <td>2.035369</td>\n",
" <td>0.0</td>\n",
" <td>4.033251</td>\n",
" <td>1.961227</td>\n",
" <td>4.05381</td>\n",
" <td>17.968470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>6.000000</td>\n",
" <td>2.000000</td>\n",
" <td>40.0</td>\n",
" <td>0.000000</td>\n",
" <td>25.000000</td>\n",
" <td>1.20000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>7.000000</td>\n",
" <td>5.000000</td>\n",
" <td>40.0</td>\n",
" <td>12.000000</td>\n",
" <td>25.000000</td>\n",
" <td>12.10000</td>\n",
" <td>43.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>12.000000</td>\n",
" <td>6.000000</td>\n",
" <td>40.0</td>\n",
" <td>14.000000</td>\n",
" <td>27.000000</td>\n",
" <td>14.70000</td>\n",
" <td>56.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17.000000</td>\n",
" <td>8.000000</td>\n",
" <td>40.0</td>\n",
" <td>14.000000</td>\n",
" <td>29.000000</td>\n",
" <td>14.70000</td>\n",
" <td>56.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22.000000</td>\n",
" <td>9.000000</td>\n",
" <td>40.0</td>\n",
" <td>14.000000</td>\n",
" <td>30.000000</td>\n",
" <td>14.70000</td>\n",
" <td>75.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Congestion Drving Speed Stops Weather \\\n",
"count 600.000000 600.000000 600.0 600.000000 600.000000 \n",
"mean 12.546667 6.253333 40.0 12.000000 27.000000 \n",
"std 5.184524 2.035369 0.0 4.033251 1.961227 \n",
"min 6.000000 2.000000 40.0 0.000000 25.000000 \n",
"25% 7.000000 5.000000 40.0 12.000000 25.000000 \n",
"50% 12.000000 6.000000 40.0 14.000000 27.000000 \n",
"75% 17.000000 8.000000 40.0 14.000000 29.000000 \n",
"max 22.000000 9.000000 40.0 14.000000 30.000000 \n",
"\n",
" Distance Travel Time \n",
"count 600.00000 600.000000 \n",
"mean 12.40400 49.786667 \n",
"std 4.05381 17.968470 \n",
"min 1.20000 3.000000 \n",
"25% 12.10000 43.000000 \n",
"50% 14.70000 56.000000 \n",
"75% 14.70000 56.000000 \n",
"max 14.70000 75.000000 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Sunday', 'Monday', 'Tuesday'], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Day'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
" ..\n",
"595 1\n",
"596 1\n",
"597 1\n",
"598 1\n",
"599 1\n",
"Name: Day, Length: 600, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data['Day'] = data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
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" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>1/1/2019</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>25</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1/2/2019</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>27</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1/3/2019</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>28</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1/4/2019</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1/5/2019</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops Weather \\\n",
"0 1/1/2019 6 0 No 5 40 14 25 \n",
"1 1/2/2019 7 0 No 8 40 14 27 \n",
"2 1/3/2019 8 0 No 9 40 14 28 \n",
"3 1/4/2019 9 0 No 7 40 14 29 \n",
"4 1/5/2019 10 0 No 6 40 14 29 \n",
"\n",
" Distance Travel Time \n",
"0 14.7 56 \n",
"1 14.7 55 \n",
"2 14.7 55 \n",
"3 14.7 56 \n",
"4 14.7 56 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data['Special'] = data['Special'].map({'No':0,'Yes':1})"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
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" <tr>\n",
" <th>595</th>\n",
" <td>8/18/2020</td>\n",
" <td>6</td>\n",
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" <td>9</td>\n",
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" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>596</th>\n",
" <td>8/19/2020</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>597</th>\n",
" <td>8/20/2020</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>598</th>\n",
" <td>8/21/2020</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>8/22/2020</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops Weather \\\n",
"595 8/18/2020 6 1 0 9 40 12 25 \n",
"596 8/19/2020 6 1 0 3 40 12 25 \n",
"597 8/20/2020 12 1 0 5 40 12 25 \n",
"598 8/21/2020 12 1 0 9 40 12 25 \n",
"599 8/22/2020 12 1 0 3 40 12 25 \n",
"\n",
" Distance Travel Time \n",
"595 12.1 49 \n",
"596 12.1 40 \n",
"597 12.1 43 \n",
"598 12.1 49 \n",
"599 12.1 40 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([25, 27, 28, 29, 30, 26], dtype=int64)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Weather'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Time', 'Day', 'Special', 'Congestion', 'Drving Speed', 'Stops',\n",
" 'Weather', 'Distance', 'Travel Time'],\n",
" dtype='object')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"X = data.drop(['Date','Travel Time'],axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"y = data['Travel Time']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train/test split\n",
"\n",
"1. split data into two parts \n",
"\n",
" training data set\n",
" testing data set\n",
"\n",
"2. Train the models on training set\n",
"\n",
"3. Test the models on testing data set"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"145 49\n",
"9 56\n",
"375 56\n",
"523 49\n",
"188 56\n",
" ..\n",
"71 40\n",
"106 73\n",
"270 56\n",
"435 34\n",
"102 56\n",
"Name: Travel Time, Length: 480, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.svm import SVR\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.neural_network import MLPClassifier\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Shiv\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"MLPClassifier()"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LinearRegression()\n",
"NN = MLPClassifier()\n",
"lr.fit(X_train,y_train)\n",
"NN.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"y_pred1 = lr.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame({'Actual':y_test, 'Lr Results':y_pred1})"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual</th>\n",
" <th>Lr Results</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>55</td>\n",
" <td>56.187653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>55</td>\n",
" <td>56.690085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>73</td>\n",
" <td>66.133304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>56</td>\n",
" <td>54.599261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>49</td>\n",
" <td>52.322530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>43</td>\n",
" <td>38.662629</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Actual Lr Results\n",
"110 55 56.187653\n",
"419 56 55.732541\n",
"565 56 53.591764\n",
"77 55 56.690085\n",
"181 73 66.133304\n",
".. ... ...\n",
"399 56 54.599261\n",
"340 56 53.591764\n",
"148 49 52.322530\n",
"494 56 55.732541\n",
"439 43 38.662629\n",
"\n",
"[120 rows x 2 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual</th>\n",
" <th>NN Results</th>\n",
" <th>LR Results</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>56.187653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>56.690085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>66.133304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>54.599261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>49</td>\n",
" <td>49</td>\n",
" <td>52.322530</td>\n",
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" <tr>\n",
" <th>494</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>43</td>\n",
" <td>43</td>\n",
" <td>38.662629</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Actual NN Results LR Results\n",
"110 55 55 56.187653\n",
"419 56 56 55.732541\n",
"565 56 56 53.591764\n",
"77 55 55 56.690085\n",
"181 73 73 66.133304\n",
".. ... ... ...\n",
"399 56 56 54.599261\n",
"340 56 56 53.591764\n",
"148 49 49 52.322530\n",
"494 56 56 55.732541\n",
"439 43 43 38.662629\n",
"\n",
"[120 rows x 3 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred2 = NN.predict(X_test)\n",
"df2 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred2, 'LR Results':y_pred1})\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x265e22fe0e0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(221)\n",
"plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
"plt.plot(df1['Lr Results'].iloc[0:11],label='Lr Results')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\tTime\tDay\tRush\tSpecial\tCongestion\tDrving Speed\tStops\tWeather\tDistance\tTravel Time"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x265e2510ca0>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(221)\n",
"plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
"plt.plot(df2['NN Results'].iloc[0:11],label='NN Results')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prediction with Current parameters (Predict Time with trained Model)"
]
},
{
"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",
" }\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>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>23</td>\n",
" <td>14.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Day Special Congestion Drving Speed Stops Weather Distance\n",
"0 6 1 0 8 40 14 23 14.7"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'Time':6,\n",
" 'Day':1,\n",
" 'Special':0,\n",
" 'Congestion':8,\n",
" 'Drving Speed':40,\n",
" 'Stops':14,\n",
" 'Weather':23,\n",
" 'Distance':14.7\n",
" }\n",
"df = pd.DataFrame(data,index=[0])\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"linear regression [61.09983093]\n",
"Neural Network [55]\n",
"Actual Travel Time :- 58\n"
]
}
],
"source": [
"new_pred = lr.predict(df)\n",
"NN_pred = NN.predict(df)\n",
"print('linear regression',new_pred)\n",
"print('Neural Network',NN_pred)\n",
"print('Actual Travel Time :- 58')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing With Current Data"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>20</td>\n",
" <td>24</td>\n",
" <td>14.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Day Special Congestion Drving Speed Stops Weather Distance\n",
"0 17 1 0 5 40 20 24 14.7"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'Time':17,\n",
" 'Day':1,\n",
" 'Special':0,\n",
" 'Congestion':5,\n",
" 'Drving Speed':40,\n",
" 'Stops':20,\n",
" 'Weather':24,\n",
" 'Distance':14.7\n",
" }\n",
"ddf = pd.DataFrame(data,index=[0])\n",
"ddf"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel Time Prediction :- 56 Min\n"
]
}
],
"source": [
"resultsNN = NN.predict(ddf)\n",
"resultsNN\n",
"print('Travel Time Prediction :-',*resultsNN,'Min')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"def GetPrediction(time,date,special,congestion,dspeed,stops,weather,distance):\n",
" data = {'Time':time,\n",
" 'Day':date,\n",
" 'Special':special,\n",
" 'Congestion':congestion,\n",
" 'Drving Speed':dspeed,\n",
" 'Stops':stops,\n",
" 'Weather':weather,\n",
" 'Distance':distance\n",
" }\n",
" ddf = pd.DataFrame(data,index=[0])\n",
" resultsNN = NN.predict(ddf)\n",
" resultsNN\n",
" print('Travel Time Prediction :-',*resultsNN,'Min')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel Time Prediction :- 55 Min\n"
]
}
],
"source": [
"def GetDateCode(date):\n",
" if('Sunday' == date ):\n",
" return 0\n",
" elif('Monday' == date):\n",
" return 1\n",
" elif('Tuesday' == date):\n",
" return 2\n",
" elif('Wednesday' == date):\n",
" return 3\n",
" elif('Thursday' == date):\n",
" return 4\n",
" elif('Friday' == date):\n",
" return 5\n",
" elif('Saturday' == date):\n",
" return 6\n",
" else:\n",
" return 9\n",
" \n",
"\n",
" \n",
"time= 6 #this time come from semins data/member of route planning\n",
"day ='Friday' #date come from UI \n",
"date = GetDateCode(day)\n",
"special = 0 # Special Day or Not 1/0\n",
"Congestion= 8 # This is depend on the depature time---- heavy traffic =9 / No Traffic = 0 \n",
"drivingspeedAVG = 40 #this is come from GPS data\n",
"stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning \n",
"weather =23 #Get weather in travel area\n",
"\n",
"\n",
"GetPrediction(time,date,special,Congestion,drivingspeedAVG,14,24,14.7)"
]
},
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