Food Delivery Time Prediction Using Regression Models.

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px

import warnings warnings.filterwarnings('ignore')

df = pd.read_csv("food_delivery.csv", encoding='latin1')

df.head()

IDobject

Delivery_person_IDobject

4607

INDORES13DEL02

B379

BANGRES18DEL02

5D6D

BANGRES19DEL01

7A6A

COIMBRES13DEL02

70A2

CHENRES12DEL01

df.tail()

IDobject

Delivery_person_IDobject

45588

7C09

JAPRES04DEL01

45589

D641

AGRRES16DEL01

45590

4F8D

CHENRES08DEL03

45591

5EEE

COIMBRES11DEL01

45592

5FB2

RANCHIRES09DEL02

df.shape

df.columns

df.duplicated().sum()

df.isnull().sum()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45593 entries, 0 to 45592
Data columns (total 11 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   ID                           45593 non-null  object 
 1   Delivery_person_ID           45593 non-null  object 
 2   Delivery_person_Age          45593 non-null  int64  
 3   Delivery_person_Ratings      45593 non-null  float64
 4   Restaurant_latitude          45593 non-null  float64
 5   Restaurant_longitude         45593 non-null  float64
 6   Delivery_location_latitude   45593 non-null  float64
 7   Delivery_location_longitude  45593 non-null  float64
 8   Type_of_order                45593 non-null  object 
 9   Type_of_vehicle              45593 non-null  object 
 10  Time_taken(min)              45593 non-null  int64  
dtypes: float64(5), int64(2), object(4)
memory usage: 3.8+ MB

df.describe()

Delivery_person_Agefloat64

Delivery_person_Ratingsfloat64

count

45593

mean

29.54407475

4.632366811

std

5.696792937

0.3277075288

min

25%

4.6

50%

4.7

75%

4.8

max

df.nunique()

object_columns = df.select_dtypes(include='object').columns print("Object Columns:") print(object_columns) print() numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns print("Numerical Columns:") print(numerical_columns)

Object Columns:
Index(['ID', 'Delivery_person_ID', 'Type_of_order', 'Type_of_vehicle'], dtype='object')

Numerical Columns:
Index(['Delivery_person_Age', 'Delivery_person_Ratings', 'Restaurant_latitude',
       'Restaurant_longitude', 'Delivery_location_latitude',
       'Delivery_location_longitude', 'Time_taken(min)'],
      dtype='object')

df['Type_of_order'].unique()

df['Type_of_order'].value_counts()

plt.figure(figsize=(15,6)) sns.countplot(x='Type_of_order', data=df, palette='hls') plt.xticks(rotation=0) plt.show()

plt.figure(figsize=(15, 6)) counts = df['Type_of_order'].value_counts() plt.pie(counts, labels=counts.index, autopct='%1.1f%%', colors=sns.color_palette('hls')) plt.title('Type_of_order') plt.show()

import plotly.graph_objects as go

fig = go.Figure(data=[go.Bar(x=df['Type_of_order'].value_counts().index, y=df['Type_of_order'].value_counts())]) fig.update_layout( title= 'Type_of_order', xaxis_title="Categories", yaxis_title="Count" ) fig.show()

counts = df['Type_of_order'].value_counts() fig = go.Figure(data=[go.Pie(labels=counts.index, values=counts)]) fig.update_layout(title= 'Type_of_order') fig.show()

df['Type_of_vehicle'].unique()

df['Type_of_vehicle'].value_counts()

plt.figure(figsize=(15,6)) sns.countplot(x='Type_of_vehicle', data=df, palette='hls') plt.xticks(rotation=0) plt.show()

plt.figure(figsize=(15, 6)) counts = df['Type_of_vehicle'].value_counts() plt.pie(counts, labels=counts.index, autopct='%1.1f%%', colors=sns.color_palette('hls')) plt.title('Type_of_vehicle') plt.show()

fig = go.Figure(data=[go.Bar(x=df['Type_of_vehicle'].value_counts().index, y=df['Type_of_vehicle'].value_counts())]) fig.update_layout( title= 'Type_of_vehicle', xaxis_title="Categories", yaxis_title="Count" ) fig.show()

counts = df['Type_of_vehicle'].value_counts() fig = go.Figure(data=[go.Pie(labels=counts.index, values=counts)]) fig.update_layout(title= 'Type_of_vehicle') fig.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.histplot(df[i], kde = True, bins = 20, palette = 'hls') plt.xticks(rotation = 90) plt.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.distplot(df[i], kde = True, bins = 20) plt.xticks(rotation = 90) plt.show()

for i in numerical_columns: plt.figure(figsize=(15, 6)) sns.boxplot(x=i, data=df, palette='hls') plt.xticks(rotation=90) plt.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.violinplot(x = i, data = df, palette = 'hls') plt.xticks(rotation = 90) plt.show()

import plotly.express as px for column in numerical_columns: fig = px.histogram(df, x=column, nbins=20, histnorm='probability density') fig.update_layout(title=f"Histogram of {column}", xaxis_title=column, yaxis_title="Probability Density") fig.show()

for column in numerical_columns: fig = px.box(df, y=column) fig.update_layout(title=f"Box Plot of {column}", yaxis_title=column) fig.show()

for column in numerical_columns: fig = px.violin(df, y=column) fig.update_layout(title=f"Violin Plot of {column}", yaxis_title=column) fig.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.barplot(x = 'Type_of_order', y = df[i], data = df, ci = None, palette = 'hls') plt.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.barplot(x = df['Type_of_vehicle'], y = df[i], data = df, ci = None, palette = 'hls') plt.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.boxplot(x = df['Type_of_order'], y = df[i], data = df, palette = 'hls') plt.show()

for i in numerical_columns: plt.figure(figsize=(15,6)) sns.violinplot(x = df['Type_of_order'], y = df[i], data = df, palette = 'hls') plt.show()

for i in numerical_columns: for j in numerical_columns: if i != j: plt.figure(figsize=(15,6)) sns.lineplot(x = df[j], y = df[i], data = df, ci = None, palette = 'hls') plt.xticks(rotation = 90) plt.show()

for i in numerical_columns: for j in numerical_columns: if i != j: plt.figure(figsize=(15, 6)) sns.scatterplot(x=df[j], y=df[i], palette='hls') plt.xticks(rotation=90) plt.show()

df_corr = df.corr()

df_corr

Delivery_person_Agefloat64

Delivery_person_Ratingsfloat64

Delivery_person_Age

-0.06744873545

Delivery_person_Ratings

-0.06744873545

Restaurant_latitude

-0.001954920525

-0.004846150689

Restaurant_longitude

-0.006795669134

-0.01114706268

Delivery_location_latitude

0.002359456452

-0.01019760264

Delivery_location_longitude

-0.0005930208697

-0.01335002877

Time_taken(min)

0.2927078591

-0.3311027209

plt.figure(figsize=(30, 10)) matrix = np.triu(df_corr) sns.heatmap(df_corr, annot=True, linewidth=.8, mask=matrix, cmap="rocket"); plt.show()

fig = go.Figure(data=go.Heatmap( z=df_corr.values, x=df_corr.columns, y=df_corr.index, colorscale='Viridis', # Use a valid colorscale name colorbar=dict(title='Correlation') )) fig.update_layout( title='Correlation Heatmap', xaxis=dict(title='X-axis labels'), yaxis=dict(title='Y-axis labels'), width=800, height=400, plot_bgcolor='white' ) fig.show()

IDobject

6E+020%

7E+010%

45449 others100%

Delivery_person_IDobject

JAPRES11DEL020.1%

PUNERES01DEL010.1%

1318 others99.7%

4607

INDORES13DEL02

B379

BANGRES18DEL02

5D6D

BANGRES19DEL01

7A6A

COIMBRES13DEL02

70A2

CHENRES12DEL01

9BB4

HYDRES09DEL03

95B4

RANCHIRES15DEL01

9EB2

MYSRES15DEL02

1102

HYDRES05DEL02

CDCD

DEHRES17DEL01

# Extracting Time Components df['hour_of_day'] = pd.to_datetime(df['Time_taken(min)'], unit='m').dt.hour df['day_of_week'] = pd.to_datetime(df['Time_taken(min)'], unit='m').dt.dayofweek df['month_of_year'] = pd.to_datetime(df['Time_taken(min)'], unit='m').dt.month

import math

# Function to calculate distance between two sets of latitude and longitude coordinates def calculate_distance(lat1, lon1, lat2, lon2): R = 6371 # Earth's radius in kilometers # Convert latitude and longitude from degrees to radians lat1_rad = math.radians(lat1) lon1_rad = math.radians(lon1) lat2_rad = math.radians(lat2) lon2_rad = math.radians(lon2) # Haversine formula to calculate distance dlat = lat2_rad - lat1_rad dlon = lon2_rad - lon1_rad a = math.sin(dlat/2) ** 2 + math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(dlon/2) ** 2 c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a)) distance = R * c return distance # Calculate distance and create the distance feature df['distance'] = df.apply(lambda row: calculate_distance(row['Restaurant_latitude'], row['Restaurant_longitude'], row['Delivery_location_latitude'], row['Delivery_location_longitude']), axis=1)

# Categorizing Age age_bins = [0, 30, 50, float('inf')] age_labels = ['young', 'middle-aged', 'senior'] df['age_category'] = pd.cut(df['Delivery_person_Age'], bins=age_bins, labels=age_labels)

# Aggregating Ratings df['avg_ratings'] = df.groupby('Delivery_person_ID')['Delivery_person_Ratings'].transform('mean')

# Binary Encoding df = pd.get_dummies(df, columns=['Type_of_order', 'Type_of_vehicle'])

# Interaction Features df['time_ratings_interaction'] = df['Time_taken(min)'] * df['Delivery_person_Ratings']

IDobject

6E+020%

7E+010%

45449 others100%

Delivery_person_IDobject

JAPRES11DEL020.1%

PUNERES01DEL010.1%

1318 others99.7%

4607

INDORES13DEL02

B379

BANGRES18DEL02

5D6D

BANGRES19DEL01

7A6A

COIMBRES13DEL02

70A2

CHENRES12DEL01

9BB4

HYDRES09DEL03

95B4

RANCHIRES15DEL01

9EB2

MYSRES15DEL02

1102

HYDRES05DEL02

CDCD

DEHRES17DEL01

columns_to_drop = ['ID', 'Delivery_person_ID', 'Restaurant_latitude', 'Restaurant_longitude', 'Delivery_location_latitude', 'Delivery_location_longitude']

# Drop the columns from the dataset df = df.drop(columns=columns_to_drop)

Delivery_person_Ageint64

15 - 50

Delivery_person_Ratingsfloat64

1.0 - 6.0

4.9

4.5

4.4

4.7

4.6

4.8

4.7

4.6

4.8

4.2

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45593 entries, 0 to 45592
Data columns (total 18 columns):
 #   Column                             Non-Null Count  Dtype   
---  ------                             --------------  -----   
 0   Delivery_person_Age                45593 non-null  int64   
 1   Delivery_person_Ratings            45593 non-null  float64 
 2   Time_taken(min)                    45593 non-null  int64   
 3   hour_of_day                        45593 non-null  int64   
 4   day_of_week                        45593 non-null  int64   
 5   month_of_year                      45593 non-null  int64   
 6   distance                           45593 non-null  float64 
 7   age_category                       45593 non-null  category
 8   avg_ratings                        45593 non-null  float64 
 9   Type_of_order_Buffet               45593 non-null  uint8   
 10  Type_of_order_Drinks               45593 non-null  uint8   
 11  Type_of_order_Meal                 45593 non-null  uint8   
 12  Type_of_order_Snack                45593 non-null  uint8   
 13  Type_of_vehicle_bicycle            45593 non-null  uint8   
 14  Type_of_vehicle_electric_scooter   45593 non-null  uint8   
 15  Type_of_vehicle_motorcycle         45593 non-null  uint8   
 16  Type_of_vehicle_scooter            45593 non-null  uint8   
 17  time_ratings_interaction           45593 non-null  float64 
dtypes: category(1), float64(4), int64(5), uint8(8)
memory usage: 3.5 MB

df.columns

from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder

features_to_scale = ['Delivery_person_Age', 'Delivery_person_Ratings', 'time_ratings_interaction'] features_not_to_scale = ['Time_taken(min)', 'hour_of_day', 'day_of_week', 'month_of_year', 'distance', 'age_category', 'avg_ratings', 'Type_of_order_Buffet ', 'Type_of_order_Drinks ', 'Type_of_order_Meal ', 'Type_of_order_Snack ', 'Type_of_vehicle_bicycle ', 'Type_of_vehicle_electric_scooter ', 'Type_of_vehicle_motorcycle ', 'Type_of_vehicle_scooter '] target = 'Time_taken(min)'

# Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df[features_to_scale + features_not_to_scale], df[target], test_size=0.25, random_state=42)

# Perform feature scaling for the appropriate features scaler = StandardScaler() X_train_scaled = X_train.copy() X_test_scaled = X_test.copy() X_train_scaled[features_to_scale] = scaler.fit_transform(X_train_scaled[features_to_scale]) X_test_scaled[features_to_scale] = scaler.transform(X_test_scaled[features_to_scale])

# Perform one-hot encoding for the 'age_category' feature ct = ColumnTransformer( [('one_hot_encoder', OneHotEncoder(), ['age_category'])], remainder='passthrough' ) X_train_scaled = ct.fit_transform(X_train_scaled) X_test_scaled = ct.transform(X_test_scaled)

# Create and train the linear regression model model_lr = LinearRegression() model_lr.fit(X_train_scaled, y_train)

# Make predictions on the test set y_pred = model_lr.predict(X_test_scaled)

# Evaluate the model using root mean squared error (RMSE) rmse = mean_squared_error(y_test, y_pred, squared=False) print('Root Mean Squared Error:', rmse)

Root Mean Squared Error: 2.341808939614661e-14

from sklearn.metrics import r2_score

# Calculate R-squared score r2_lr = r2_score(y_test, y_pred) print('R-squared Score:', r2_lr) # Calculate Mean Squared Error (MSE) mse = mean_squared_error(y_test, y_pred) print('Mean Squared Error:', mse)

R-squared Score: 1.0
Mean Squared Error: 5.484069109659143e-28

!pip install xgboost==1.7.6

Requirement already satisfied: xgboost==1.7.6 in /root/venv/lib/python3.9/site-packages (1.7.6)
Requirement already satisfied: scipy in /shared-libs/python3.9/py/lib/python3.9/site-packages (from xgboost==1.7.6) (1.9.3)
Requirement already satisfied: numpy in /shared-libs/python3.9/py/lib/python3.9/site-packages (from xgboost==1.7.6) (1.23.4)
WARNING: You are using pip version 22.0.4; however, version 23.2 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.

from sklearn.tree import DecisionTreeRegressor from xgboost import XGBRegressor

# Create and train the Decision Tree Regressor dt_regressor = DecisionTreeRegressor(random_state=42) dt_regressor.fit(X_train_scaled, y_train)

# Make predictions on the test set using Decision Tree Regressor y_pred_dt = dt_regressor.predict(X_test_scaled)

# Calculate R-squared score for Decision Tree Regressor r2_dt = r2_score(y_test, y_pred_dt) print('Decision Tree Regressor - R-squared Score:', r2_dt)

Decision Tree Regressor - R-squared Score: 1.0

# Calculate Mean Squared Error (MSE) for Decision Tree Regressor mse_dt = mean_squared_error(y_test, y_pred_dt) print('Decision Tree Regressor - Mean Squared Error:', mse_dt)

Decision Tree Regressor - Mean Squared Error: 0.0

# Create and train the XGBoost Regressor xgb_regressor = XGBRegressor(random_state=42) xgb_regressor.fit(X_train_scaled, y_train)

# Make predictions on the test set using XGBoost Regressor y_pred_xgb = xgb_regressor.predict(X_test_scaled)

# Calculate R-squared score for XGBoost Regressor r2_xgb = r2_score(y_test, y_pred_xgb) print('XGBoost Regressor - R-squared Score:', r2_xgb) # Calculate Mean Squared Error (MSE) for XGBoost Regressor mse_xgb = mean_squared_error(y_test, y_pred_xgb) print('XGBoost Regressor - Mean Squared Error:', mse_xgb)

XGBoost Regressor - R-squared Score: 0.999999999991573
XGBoost Regressor - Mean Squared Error: 7.388065861443196e-10