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2022-020
Commit cd4ab252 authored by W.P.S.M wickramage's avatar W.P.S.M wickramage
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TrainPartAdded

parent 937fbe16
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Showing with 902 additions and 3 deletions
.vscode/settings.json 0 → 100644
View file @ cd4ab252
{
"python.analysis.extraPaths": [
"./TravelTimePrediction/TrainData"
]
}
\ No newline at end of file
TravelTimePrediction/TrainData/TrainData.csv 0 → 100644
View file @ cd4ab252
Dipature,Arrival,Time
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
Dehiwala,Fort ,25min
Dehiwala,wellawatte,5min
Dehiwala,bambalapitiya,9min
Dehiwala,kollupitiya,13min
Dehiwala,kompannavidiya,16min
wellawatte,bambalapitiya,4min
wellawatte,kollupitiya,8min
wellawatte,kompannavidiya,11min
wellawatte,Fort ,13min
bambalapitiya,kollupitiya,4min
bambalapitiya,kompannavidiya,7min
bambalapitiya,Fort ,9min
kollupitiya,kompannavidiya,3min
kollupitiya,Fort ,5min
kompannavidiya,Fort ,3min
TravelTimePrediction/TrainData/TrainDataSet.txt 0 → 100644
View file @ cd4ab252
Dehiwala 1
wellawatte 2
bambalapitiya 3
kollupitiya 4
kompannavidiya 5
Fort 6
TravelTimePrediction/TrainData/TrainTimePrediction.ipynb 0 → 100644
View file @ cd4ab252
{
"cells": [
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('TrainData.csv')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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>Dipature</th>\n",
" <th>Arrival</th>\n",
" <th>Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Dehiwala</td>\n",
" <td>Fort</td>\n",
" <td>25min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Dehiwala</td>\n",
" <td>wellawatte</td>\n",
" <td>5min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Dehiwala</td>\n",
" <td>bambalapitiya</td>\n",
" <td>9min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Dehiwala</td>\n",
" <td>kollupitiya</td>\n",
" <td>13min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dehiwala</td>\n",
" <td>kompannavidiya</td>\n",
" <td>16min</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Dipature Arrival Time\n",
"0 Dehiwala Fort 25min\n",
"1 Dehiwala wellawatte 5min\n",
"2 Dehiwala bambalapitiya 9min\n",
"3 Dehiwala kollupitiya 13min\n",
"4 Dehiwala kompannavidiya 16min"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 150 entries, 0 to 149\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Dipature 150 non-null object\n",
" 1 Arrival 150 non-null object\n",
" 2 Time 150 non-null object\n",
"dtypes: object(3)\n",
"memory usage: 3.6+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Dehiwala', 'wellawatte', 'bambalapitiya', 'kollupitiya',\n",
" 'kompannavidiya'], dtype=object)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Dipature'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 1\n",
"2 1\n",
"3 1\n",
"4 1\n",
" ..\n",
"145 3\n",
"146 3\n",
"147 4\n",
"148 4\n",
"149 5\n",
"Name: Dipature, Length: 150, dtype: int64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Dipature'].map({'Dehiwala':1,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"data['Dipature'] = data['Dipature'].map({'Dehiwala':1,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Fort ', 'wellawatte', 'bambalapitiya', 'kollupitiya',\n",
" 'kompannavidiya'], dtype=object)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Arrival'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 6\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
" ..\n",
"145 5\n",
"146 6\n",
"147 5\n",
"148 6\n",
"149 6\n",
"Name: Arrival, Length: 150, dtype: int64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Arrival'].map({'Fort ':6,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"data['Arrival'] = data['Arrival'].map({'Fort ':6,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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>Dipature</th>\n",
" <th>Arrival</th>\n",
" <th>Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>25min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>9min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>13min</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>16min</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Dipature Arrival Time\n",
"0 1 6 25min\n",
"1 1 2 5min\n",
"2 1 3 9min\n",
"3 1 4 13min\n",
"4 1 5 16min"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"X = data.drop(['Time'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"y = data['Time']"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 76,
"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": 77,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Shiv\\AppData\\Local\\Programs\\Python\\Python310\\lib\\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-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 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-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 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-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 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-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 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-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" 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-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" 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": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NN = MLPClassifier()\n",
"NN.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"y_pred1 = NN.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred1})"
]
},
{
"cell_type": "code",
"execution_count": 81,
"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>Dipature</th>\n",
" <th>Arrival</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Dipature Arrival\n",
"0 1 2"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'Dipature':1,\n",
" 'Arrival':2,\n",
" }\n",
"df = pd.DataFrame(data,index=[0])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Travel Time Prediction :- 5min \n"
]
}
],
"source": [
"predit = NN.predict(df)\n",
"print('Train Travel Time Prediction :- ',*predit,' ')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"def GetTrainPrediction(Dipature,Arrival):\n",
" data = {'Dipature':Dipature,\n",
" 'Arrival':Arrival,\n",
" }\n",
" df = pd.DataFrame(data,index=[0])\n",
" predit = NN.predict(df)\n",
" print('Train Travel Time Prediction :- ',*predit,' ')"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"def GetDipatureCode(data):\n",
" if('Dehiwala' == data):\n",
" return 1\n",
" elif('wellawatte' == data):\n",
" return 2\n",
" elif('bambalapitiya' == data):\n",
" return 3\n",
" elif('kollupitiya' == data):\n",
" return 4\n",
" elif('kompannavidiya' == data):\n",
" return 5"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"def GetArrivalCode(data):\n",
" if('Fort' == data):\n",
" return 6\n",
" elif('wellawatte' == data):\n",
" return 2\n",
" elif('bambalapitiya' == data):\n",
" return 3\n",
" elif('kollupitiya' == data):\n",
" return 4\n",
" elif('kompannavidiya' == data):\n",
" return 5"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Travel Time Prediction :- 25min \n"
]
}
],
"source": [
"Dipature = GetDipatureCode('Dehiwala')\n",
"Arrival = GetArrivalCode('Fort')\n",
"\n",
"GetTrainPrediction(Dipature,Arrival)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.7 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "5cf8c3d3e58a91b903f819a9c98dd2220f70e367ec47969cb612bf546ce9e823"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
TravelTimePrediction/TrainData/TrainTimePrediction.py 0 → 100644
View file @ cd4ab252
# %%
import pandas as pd
# %%
data = pd.read_csv('TravelTimePrediction\TrainData\TrainData.csv')
# %%
# %%
# %%
# %%
data['Dipature'].map({'Dehiwala':1,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
# %%
data['Dipature'] = data['Dipature'].map({'Dehiwala':1,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
# %%
# %%
data['Arrival'].map({'Fort ':6,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
# %%
data['Arrival'] = data['Arrival'].map({'Fort ':6,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
# %%
# %%
X = data.drop(['Time'],axis=1)
# %%
y = data['Time']
# %%
from sklearn.model_selection import train_test_split
# %%
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
# %%
from sklearn.neural_network import MLPClassifier
# %%
NN = MLPClassifier()
NN.fit(X_train,y_train)
# %%
y_pred1 = NN.predict(X_test)
# %%
df1 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred1})
# %%
data = {'Dipature':1,
'Arrival':2,
}
df = pd.DataFrame(data,index=[0])
# %%
predit = NN.predict(df)
# %%
def GetTrainPrediction(Dipature,Arrival):
data = {'Dipature':Dipature,
'Arrival':Arrival,
}
df = pd.DataFrame(data,index=[0])
predit = NN.predict(df)
print('Train Travel Time Prediction :- ',*predit,' ')
# %%
def GetDipatureCode(data):
if('Dehiwala' == data):
return 1
elif('wellawatte' == data):
return 2
elif('bambalapitiya' == data):
return 3
elif('kollupitiya' == data):
return 4
elif('kompannavidiya' == data):
return 5
# %%
def GetArrivalCode(data):
if('Fort' == data):
return 6
elif('wellawatte' == data):
return 2
elif('bambalapitiya' == data):
return 3
elif('kollupitiya' == data):
return 4
elif('kompannavidiya' == data):
return 5
# %%
def GetPrediction(Dvalue,Avalue):
Dipature = GetDipatureCode(Dvalue)
Arrival = GetArrivalCode(Avalue)
GetTrainPrediction(Dipature,Arrival)
GetPrediction('Dehiwala','Fort')
\ No newline at end of file
TravelTimePrediction.ipynb → TravelTimePrediction/TravelTimePrediction.ipynb
View file @ cd4ab252
...@@ -1668,7 +1668,7 @@ ...@@ -1668,7 +1668,7 @@
"orig_nbformat": 4, "orig_nbformat": 4,
"vscode": { "vscode": {
"interpreter": { "interpreter": {
"hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77" "hash": "5cf8c3d3e58a91b903f819a9c98dd2220f70e367ec47969cb612bf546ce9e823"
} }
} }
}, },
......
TravelTimePredictionNN.py → TravelTimePrediction/TravelTimePredictionNN.py
View file @ cd4ab252
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
import pandas as pd import pandas as pd
# %% # %%
data = pd.read_csv('BusTravelData.csv') data = pd.read_csv('TravelTimePrediction\BusTravelData.csv')
# %% # %%
...@@ -180,6 +180,8 @@ def GetDateCode(date): ...@@ -180,6 +180,8 @@ def GetDateCode(date):
import testingWeather import testingWeather
city = "malabe" city = "malabe"
city = city+" weather" city = city+" weather"
...@@ -187,7 +189,7 @@ time= 6 #this time come from semins data/member of route planning ...@@ -187,7 +189,7 @@ time= 6 #this time come from semins data/member of route planning
day ='Friday' #date come from UI day ='Friday' #date come from UI
date = GetDateCode(day) date = GetDateCode(day)
special = 0 # Special Day or Not 1/0 special = 0 # Special Day or Not 1/0
Congestion= 8 # This is depend on the depature time---- heavy traffic =9 / No Traffic = 0 Congestion= 8 # This is come from TomTOm API / APK app
drivingspeedAVG = 40 #this is come from GPS data drivingspeedAVG = 40 #this is come from GPS data
stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning
weather =testingWeather.weather(city) #Get weather in travel area weather =testingWeather.weather(city) #Get weather in travel area
...@@ -198,5 +200,17 @@ GetPrediction(time,date,special,Congestion,drivingspeedAVG,stops,weather,Distanc ...@@ -198,5 +200,17 @@ GetPrediction(time,date,special,Congestion,drivingspeedAVG,stops,weather,Distanc
# Get Train Data
# import sys
# adding Folder_2/subfolder to the system path
# sys.path.insert(0, 'TravelTimePrediction\TrainData')
# importing the hello
# from TrainTimePrediction import GetPrediction
# GetPrediction('Dehiwala','Fort')
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