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demand_forecasting_big_mart_sales_prediction.py
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demand_forecasting_big_mart_sales_prediction.py
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# -*- coding: utf-8 -*-
"""Demand Forecasting - Big Mart Sales Prediction.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1UVCog0KtKpSizOREnqnLICa_vYO241WR
### Case Study: Big Mart Sales Prediction
**importing the dependencies**
"""
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 and analysis
"""
big_mart_data = pd.read_csv('/content/Train.csv')
#first five rows of dataframe
big_mart_data.head()
#no. of rows and columns i.e, products and features
big_mart_data.shape
#getting some informations 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 missing values
big_mart_data.isnull().sum()
"""Handling Missing values (imputation)
"""
#mean value of item weight column
big_mart_data['Item_Weight'].mean()
#filling missing values in item weight column with mean value
big_mart_data['Item_Weight'].fillna(big_mart_data['Item_Weight'].mean(), inplace=True)
#checking if its updated
big_mart_data.isnull().sum()
"""**Replacing the missing values in outlet size column with mode **
(The missing values in the "outlet size" column will be filled by the mode since the data is categorical)
"""
big_mart_data['Outlet_Size'].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)
missing_values = big_mart_data['Outlet_Size'].isnull()
missing_values
big_mart_data.loc[missing_values, 'Outlet_Size'] = big_mart_data.loc[missing_values,'Outlet_Type'].apply(lambda x: mode_of_Outlet_size[x])
big_mart_data.isnull().sum()
"""# Data Analysis"""
#statistical measures about the data
big_mart_data.describe()
"""**Numerical Features**"""
sns.set()
#item weight colum distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_Weight'])
plt.show()
#item visibility colum distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_Visibility'])
plt.show()
#item mrp colum distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_MRP'])
plt.show()
#item Item_Outlet_Sales colum distribution
plt.figure(figsize=(6,6))
sns.distplot(big_mart_data['Item_Outlet_Sales'])
plt.show()
#item output establishment year colum distribution
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()
# Outlet_Size column
plt.figure(figsize=(6,6))
sns.countplot(x='Outlet_Size', 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()
"""**Label Encoding**
convert categorical values and transform them into numerical value
"""
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'])
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()
"""**Splitting features and Target**"""
X = big_mart_data.drop(columns='Item_Outlet_Sales', axis=1)
Y = big_mart_data['Item_Outlet_Sales']
X
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**
XGBoostRegressor
"""
regressor = XGBRegressor()
regressor.fit(X_train,Y_train)
"""**Evaluation**"""
#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)
# Calculate Mean Absolute Error (MAE)
mae = metrics.mean_absolute_error(Y_test, test_data_prediction)
print('Mean Absolute Error (MAE) = ', mae)
# Calculate Root Mean Squared Error (RMSE)
rmse = np.sqrt(metrics.mean_squared_error(Y_test, test_data_prediction))
print('Root Mean Squared Error (RMSE) = ', rmse)