Unlock the power of sales prediction with Python! This tutorial delves into regression techniques for Black Friday sales analysis. Learn to build accurate models that forecast sales, gain insights into customer behavior, and optimize pricing strategies. Enhance your skills in machine learning, data analysis, and uncover valuable insights for business success. Dive into the realm of Black Friday sales prediction with this hands-on project tutorial. #BlackFridaySales #Python #Regression #MachineLearning #SalesPrediction #DataAnalysis #BusinessInsights
In this project tutorial, we analyze and predict the sales during Black Friday, and display the results through plot graphs and different prediction models.
You can watch the video-based tutorial with step by step explanation down below
Dataset Information
This dataset comprises of sales transactions captured at a retail store. It’s a classic dataset to explore and expand your feature engineering skills and day to day understanding from multiple shopping experiences. This is a regression problem. The dataset has 550,069 rows and 12 columns.
Problem: Predict purchase amount
Attributes:
Masked attributes hide the data information.
Download the Dataset here
Import modules
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
%matplotlib inline
warnings.filterwarnings('ignore')pandas - used to perform data manipulation and analysis
numpy - used to perform a wide variety of mathematical operations on arrays
matplotlib - used for data visualization and graphical plotting
seaborn - built on top of matplotlib with similar functionalities
%matplotlib - to enable the inline plotting.
warnings - to manipulate warnings details
filterwarnings('ignore') is to ignore the warnings thrown by the modules (gives clean results)
Loading the dataset
df = pd.read_csv('train.csv')
df.head()
Some columns have null values, those values must be replaced for a relevant value for further processing.
Let us see the statistical information of the attributes
# statistical info
df.describe()
Statistical information of the data
Product_Category_2 and Product_Category_3 have lower number of samples than Product_Category_1, both could be sub categories.
Let us see the data type information of the attributes
# datatype info
df.info()
We have categorical as well as numerical attributes which we will process separately.
Product_Category_1 data type is different from Product_Category_2 and Product_Category_3, that won't affect the process or the result.
# find unique values
df.apply(lambda x: len(x.unique()))
Attributes containing many unique values are of numerical type. The remaining attributes are of categorical type.
Exploratory Data Analysis
# distplot for purchase
plt.style.use('fivethirtyeight')
plt.figure(figsize=(13, 7))
sns.distplot(df['Purchase'], bins=25)
First part of the graph has a normal distribution and later forming some peaks in the graph
Evaluating the whole graph, it has a normal distribution
# distribution of numeric variables
sns.countplot(df['Gender'])
Many buyers are male while the minority are female.
Difference is due to the categories on sale during Black Friday, evaluating a particular category may change the count between genders.
sns.countplot(df['Age'])
There are 7 categories defined to classify the age of the buyers
sns.countplot(df['Marital_Status'])
Majority of the buyers are single
sns.countplot(df['Occupation'])
Display of the occupation of the buyers
Occupation 8 has extremely low count compared with the others; it can be ignored for the calculation since it won't affect much the result.
sns.countplot(df['Product_Category_1'])
Majority of the products are in category 1, 5 and 8.
The low no. categories can be combined into a single category to greatly reduce the complexity of the problem.
sns.countplot(df['Product_Category_2'])
Categories are in float values
Categories 2, 8, 14 to 16 are higher compared with the others.
sns.countplot(df['Product_Category_3'])
Categories are in float values
Categories 14 to 17 are higher
sns.countplot(df['City_Category'])
Higher count might represent the urban area indicates more population
sns.countplot(df['Stay_In_Current_City_Years'])
Most buyers have one year living in the city
Remaining categories are uniform distribution
Now let us plot using two variables for analysis
# bivariate analysis
occupation_plot = df.pivot_table(index='Occupation', values='Purchase', aggfunc=np.mean)
occupation_plot.plot(kind='bar', figsize=(13, 7))
plt.xlabel('Occupation')
plt.ylabel("Purchase")
plt.title("Occupation and Purchase Analysis")
plt.xticks(rotation=0)
plt.show()
np.mean will display mean of the purchase based on occupation
np.sum will display a sum of the purchase based on occupation
Based on the labels, we can observe all the categories being purchased in an average manner.
Recommended plot graph for presentation
age_plot = df.pivot_table(index='Age', values='Purchase', aggfunc=np.mean)
age_plot.plot(kind='bar', figsize=(13, 7))
plt.xlabel('Age')
plt.ylabel("Purchase")
plt.title("Age and Purchase Analysis")
plt.xticks(rotation=0)
plt.show()
Age and Purchase graph also has a uniform distribution.
gender_plot = df.pivot_table(index='Gender', values='Purchase', aggfunc=np.mean)
gender_plot.plot(kind='bar', figsize=(13, 7))
plt.xlabel('Gender')
plt.ylabel("Purchase")
plt.title("Gender and Purchase Analysis")
plt.xticks(rotation=0)
plt.show()
Uniform distribution but with a little difference
Preprocessing the dataset
We must check first for null values in the data
# check for null values
df.isnull().sum()
Null values are present in Product_Category_2 and Product_Category_3
Null values must be filled for easier processing
Now we fill the Null values in the dataset
df['Product_Category_2'] = df['Product_Category_2'].fillna(-2.0).astype("float32")
df['Product_Category_3'] = df['Product_Category_3'].fillna(-2.0).astype("float32")Null values filled with a negative value to not affect the results.
The value filled must be of same data type of the attribute.
Let us double check the null values
df.isnull().sum()
Now we must convert the categorical attributes to numerical using a dictionary
# encoding values using dict
gender_dict = {'F':0, 'M':1}
df['Gender'] = df['Gender'].apply(lambda x: gender_dict[x])
df.head()
'F' now converted to numerical zero (0), same for 'M' to one (1)
Label encoding is to convert the categorical column into the numerical column a lot quicker
# to improve the metric use one hot encoding
# label encoding
cols = ['Age', 'City_Category', 'Stay_In_Current_City_Years']
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for col in cols:
df[col] = le.fit_transform(df[col])
df.head()
One hot encoding increases the no. of columns but improves accuracy
More columns means more data to train, it will increase the training time
All categorical columns converted to numerical
For the input User_ID and Product_ID must be removed in order to generalize the results.
Correlation Matrix
A correlation matrix is a table showing correlation coefficients between variables. Each cell in the table shows the correlation between two variables. The value is in the range of -1 to 1. If two variables have a high correlation, we can neglect one variable from those two.
corr = df.corr()
plt.figure(figsize=(14,7))
sns.heatmap(corr, annot=True, cmap='coolwarm')
Purchase is most correlated to Product_Category_1 and Product_Category_3
Marital_Status and Age also has positive correlation
Input Split
df.head()
User_ID and Product_ID must be removed for better results, if not the results will be biased to User_ID or Product_ID
Now we split the data for training
X = df.drop(columns=['User_ID', 'Product_ID', 'Purchase'])
y = df['Purchase']Purchase is an output data that is why it is removed from X as well
Model Training
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
def train(model, X, y):
# train-test split
x_train, x_test, y_train, y_test = train_test_split(X, y,
random_state=42, test_size=0.25)
model.fit(x_train, y_train)
# predict the results
pred = model.predict(x_test)
# cross validation
cv_score = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5)
cv_score = np.abs(np.mean(cv_score))
print("Results")
print("MSE:", np.sqrt(mean_squared_error(y_test, pred)))
print("CV Score:", np.sqrt(cv_score))cross val score() is used for better validation of the model.
cv=5 means that the cross-validation will split the data into 5 parts for training.
np.abs() will convert the negative score to positive and np.mean() will give the average value of 5 scores.
Now we display the basic models
from sklearn.linear_model import LinearRegression
model = LinearRegression(normalize=True)
train(model, X, y)
coef = pd.Series(model.coef_, X.columns).sort_values()
coef.plot(kind='bar', title='Model Coefficients')Results MSE: 4617.994034201719 CV Score: 4625.252945835687
Linear Regression model must have normalized data to give better results
Gender category has high coefficient for the Linear Regression model
from sklearn.tree import DecisionTreeRegressor
model = DecisionTreeRegressor()
train(model, X, y)
features = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)
features.plot(kind='bar', title='Feature Importance')Results MSE: 3366.9672356860747 CV Score: 3338.5905886644855
Results have improved compared to Linear Regression model
Product_Category_1 has high feature importance compared to the Linear Regression model.
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_jobs=-1)
train(model, X, y)
features = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)
features.plot(kind='bar', title='Feature Importance')Results MSE: 3062.66041010778 CV Score: 3052.7778119222253
Better results compared with Decision Tree Regressor
from sklearn.ensemble import ExtraTreesRegressor
model = ExtraTreesRegressor(n_jobs=-1)
train(model, X, y)
features = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)
features.plot(kind='bar', title='Feature Importance')Results MSE: 3194.0770038232185 CV Score: 3180.5476561931346
Better results than Linear Regression but less than Random Forest Regression
Note: this prediction steps are just an example, modify it accordingly for your own usage
pred = model.predict(x_test)
submission = pd.DataFrame()
submission['User_ID'] = x_test['User_ID']
submission['Purchase'] = pred
submission.to_csv('submission.csv', index=False)Prediction of the attributes from the x_test data
Creation of a new dataframe for submission of prediction results
Stores an array with User_ID and the Purchase predictions of that buyer.
Final Thoughts
Out of the 4 models, Random Forest Regressor is the top performer with the least MSE and cv score.
You can also use hyperparameter tuning to improve the model performance.
You can further try other models like XGBoost, CatBoost etc.
In this project tutorial, we have explored the Black Friday Sales Prediction. We also compared different models to train the data starting from basic to advanced models.
Get the project notebook from here
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