Big-Mart-Sales-Prediction-/Big Mart Sales Prediction.txt at main · akulsriv123/Big-Mart-Sales-Prediction- · GitHub

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn import metrics

"""Data Collection & Analysis"""

#loading the dataset from csv file to a Pandas Dataframe
big_mart_data = pd.read_csv('/content/Train.csv')

# first five rows of the dataframe
big_mart_data.head()

# number of datapoints & number of features
big_mart_data.shape

# getting some information about the dataset
big_mart_data.info()

"""Categorical Features

Item_Identifier,
Item_Fat_Content,
Item_Type,
Outlet_Identifier,
Outlet_Size,
Outlet_Location_Type,
Outlet_Type

"""

#checking for Missing Values
big_mart_data.isnull().sum()

"""**Handling Missing Values**

Mean-->average value,
Mode-->Most repeated value
"""

# mean value of 'Item_Weight' column
big_mart_data['Item_Weight'].mean()

#filling the missing values in "Item_Weight" column with Mean value
big_mart_data['Item_Weight'].fillna(big_mart_data['Item_Weight'].mean(), inplace = True)

# mode of "Outlet_Size" column
big_mart_data['Outlet_Size'].mode()

"""**Replacing the Missing Values in 'Outlet_Size' with mode**"""

mode_of_outlet_size = big_mart_data.pivot_table(values = 'Outlet_Size', columns = 'Outlet_Type', aggfunc = (lambda x: x.mode()[0]))

print(mode_of_outlet_size)

miss_values = big_mart_data['Outlet_Size'].isnull()

print(miss_values)

big_mart_data.loc[miss_values, 'Outlet_Size'] = big_mart_data.loc[miss_values, 'Outlet_Type'].apply(lambda x: mode_of_outlet_size)

#checking for Missing Values
big_mart_data.isnull().sum()

"""**Data Analysis**"""

big_mart_data.describe()

"""Numerical Features"""

sns.set()

# Item_Weight distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_Weight'])
plt.show()

# Item Visibility distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_Visibility'])
plt.show()

# Item MRP distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_MRP'])
plt.show()

# Item_Outlet_Sales distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_Outlet_Sales'])
plt.show()

# Outlet_Establishment_Year column
plt.figure(figsize=(6,6))
sns.countplot(x='Outlet_Establishment_Year', data=big_mart_data)
plt.show()

"""Categorical Features"""

# Item_Fat_Content column
plt.figure(figsize=(6,6))
sns.countplot(x='Item_Fat_Content', data=big_mart_data)
plt.show()

# Item_Type column
plt.figure(figsize=(30,6))
sns.countplot(x='Item_Type', data=big_mart_data)
plt.show()

"""Data Pre-Processing"""

big_mart_data.head()

big_mart_data['Item_Fat_Content'].value_counts()

big_mart_data.replace({'Item_Fat_Content': {'low fat':'Low Fat','LF':'Low Fat', 'reg':'Regular'}}, inplace=True)

big_mart_data['Item_Fat_Content'].value_counts()

"""Splitting features and Target"""

encoder = LabelEncoder()

big_mart_data['Item_Identifier'] = encoder.fit_transform(big_mart_data['Item_Identifier'])

big_mart_data['Item_Fat_Content'] = encoder.fit_transform(big_mart_data['Item_Fat_Content'])

big_mart_data['Item_Type'] = encoder.fit_transform(big_mart_data['Item_Type'])

big_mart_data['Outlet_Identifier'] = encoder.fit_transform(big_mart_data['Outlet_Identifier'])

big_mart_data['Outlet_Size'] = encoder.fit_transform(big_mart_data['Outlet_Size'].astype(str))

big_mart_data['Outlet_Location_Type'] = encoder.fit_transform(big_mart_data['Outlet_Location_Type'])

big_mart_data['Outlet_Type'] = encoder.fit_transform(big_mart_data['Outlet_Type'])

big_mart_data.head()

X = big_mart_data.drop(columns='Item_Outlet_Sales', axis=1)
Y = big_mart_data['Item_Outlet_Sales']

print(X)

print(Y)

"""Splitting the data into Training data & Testing Data"""

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)

print(X.shape, X_train.shape, X_test.shape)

"""Machine Learning Model Training

XGBoost Regressor
"""

regressor = XGBRegressor()

regressor.fit(X_train, Y_train)

"""Evaluation"""

# prediction on training data
training_data_prediction = regressor.predict(X_train)

# R squared Value
r2_train = metrics.r2_score(Y_train, training_data_prediction)

print('R Squared value = ', r2_train)

# prediction on test data
test_data_prediction = regressor.predict(X_test)

# R squared Value
r2_test = metrics.r2_score(Y_test, test_data_prediction)

print('R Squared value = ', r2_test)