Objective: -
Black Friday refers to the day after the U.S. Thanksgiving holiday, which has also traditionally been a holiday itself for many employees. It is typically a day full of special shopping deals and big discounts and is considered the beginning of the holiday shopping season. The sales made on Black Friday are often thought of as a litmus test for the overall economic condition of the country and a way for economists to measure the confidence of the average American when it comes to discretionary spending. Those who share the Keynesian assumption that spending drives economic activity view lower Black Friday sales figures as a harbinger of slower growth.
Black Friday is followed by a weekend and thus it is considered the best time for businesses to attract customers with mega sales and discounts. Brands offer in-store deals on a variety of items including electronics, clothing, home appliances, and toys among others. The advent of e-commerce has added to the Black Friday frenzy and popularised the day in countries other than America.
The goal of this challenge is to use this data to train a machine learning model to predict the purchases on black friday.
Step 1: Import all the required libraries
Pandas : In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis and storing in a proper way. In particular, it offers data structures and operations for manipulating numerical tables and time series
Sklearn : Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.
Pickle : Python pickle module is used for serializing and de-serializing a Python object structure. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script.
Seaborn : Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Matplotlib : Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
#Loading libraries
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
import sklearn.linear_model
import sklearn
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.preprocessing import scale
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.decomposition import PCA
import warnings
warnings.filterwarnings('ignore')
Step 2 : Read dataset and basic details of dataset
Goal:- In this step we are going to read the dataset, view the dataset and analysis the basic details like total number of rows and columns, what are the column data types and see to need to create new column or not.
In this stage we are going to read our problem dataset and have a look on it.
#loading the dataset
try:
df = pd.read_csv('C:/Users/YAJENDRA/Documents/final notebooks/Black Friday Sales Prediction/Data/data.csv') #Path for the file
print('Data read done successfully...')
except (FileNotFoundError, IOError):
print("Wrong file or file path")
Data read done successfully...
# To view the content inside the dataset we can use the head() method that returns a specified number of rows, string from the top.
# The head() method returns the first 5 rows if a number is not specified.
df.head()
Dataset: -
Attribute Information:
- Purchase: Purchase amount.
Other features:
User_ID: Unique ID of the user.
Product_ID: Unique ID of the product.
Gender: indicates the gender of the person making the transaction.
Age: indicates the age group of the person making the transaction.
Occupation: shows the occupation of the user, already labeled with numbers 0 to 20.
City_Category: User’s living city category. Cities are categorized into 3 different categories ‘A’, ‘B’ and ‘C’.
Stay_In_Current_City_Years: Indicates how long the users has lived in this city.
Marital_Status: is 0 if the user is not married and 1 otherwise.
Product_Category_1 to _3: Category of the product. All 3 are already labaled with numbers.
Step3: Data Preprocessing
Why need of Data Preprocessing?
Preprocessing data is an important step for data analysis. The following are some benefits of preprocessing data:
It improves accuracy and reliability. Preprocessing data removes missing or inconsistent data values resulting from human or computer error, which can improve the accuracy and quality of a dataset, making it more reliable.
It makes data consistent. When collecting data, it’s possible to have data duplicates, and discarding them during preprocessing can ensure the data values for analysis are consistent, which helps produce accurate results.
It increases the data’s algorithm readability. Preprocessing enhances the data’s quality and makes it easier for machine learning algorithms to read, use, and interpret it.
After we read the data, we can look at the data using:
# count the total number of rows and columns.
print ('The train data has {0} rows and {1} columns'.format(df.shape[0],df.shape[1]))
The train data has 550068 rows and 12 columns
By analysing the problem statement and the dataset, we get to know that the target variable is “Purchase” column which is continuous and shows the sales on black friday.
df['Purchase'].value_counts()
7011 191
7193 188
6855 187
6891 184
7012 183
...
23491 1
18345 1
3372 1
855 1
21489 1
Name: Purchase, Length: 18105, dtype: int64
The df.value_counts() method counts the number of types of values a particular column contains.
df.shape
(550068, 12)
The df.shape method shows the shape of the dataset.
We can identify that their are 550068 rows and 12 columns.
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 550068 entries, 0 to 550067
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 User_ID 550068 non-null int64
1 Product_ID 550068 non-null object
2 Gender 550068 non-null object
3 Age 550068 non-null object
4 Occupation 550068 non-null int64
5 City_Category 550068 non-null object
6 Stay_In_Current_City_Years 550068 non-null object
7 Marital_Status 550068 non-null int64
8 Product_Category_1 550068 non-null int64
9 Product_Category_2 376430 non-null float64
10 Product_Category_3 166821 non-null float64
11 Purchase 550068 non-null int64
dtypes: float64(2), int64(5), object(5)
memory usage: 50.4+ MB
The df.info() method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.
df.iloc[1]
User_ID 1000001
Product_ID P00248942
Gender F
Age 0-17
Occupation 10
City_Category A
Stay_In_Current_City_Years 2
Marital_Status 0
Product_Category_1 1
Product_Category_2 6.0
Product_Category_3 14.0
Purchase 15200
Name: 1, dtype: object
df.iloc[ ] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. The iloc property gets, or sets, the value(s) of the specified indexes.
Data Type Check for every column
Why data type check is required?
Data type check helps us with understanding what type of variables our dataset contains. It helps us with identifying whether to keep that variable or not. If the dataset contains contiguous data, then only float and integer type variables will be beneficial and if we have to classify any value then categorical variables will be beneficial.
objects_cols = ['object']
objects_lst = list(df.select_dtypes(include=objects_cols).columns)
print("Total number of categorical columns are ", len(objects_lst))
print("There names are as follows: ", objects_lst)
Total number of categorical columns are 5
There names are as follows: ['Product_ID', 'Gender', 'Age', 'City_Category', 'Stay_In_Current_City_Years']
int64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)
print("Total number of numerical columns are ", len(int64_lst))
print("There names are as follows: ", int64_lst)
Total number of numerical columns are 5
There names are as follows: ['User_ID', 'Occupation', 'Marital_Status', 'Product_Category_1', 'Purchase']
float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)
print("Total number of numerical columns are ", len(float64_lst))
print("There names are as follows: ", float64_lst)
Total number of numerical columns are 2
There names are as follows: ['Product_Category_2', 'Product_Category_3']
Step 2 Insights: -
- We have total 12 features where 5 of them are integer type, 5 are object type while others are float type.
After this step we have to calculate various evaluation parameters which will help us in cleaning and analysing the data more accurately.
Step 3: Descriptive Analysis
Goal/Purpose: Finding the data distribution of the features. Visualization helps to understand data and also to explain the data to another person.
Things we are going to do in this step:
Mean
Median
Mode
Standard Deviation
Variance
Null Values
NaN Values
Min value
Max value
Count Value
Quatilers
Correlation
Skewness
df.describe()
The df.describe() method returns description of the data in the DataFrame. If the DataFrame contains numerical data, the description contains these information for each column: count — The number of not-empty values. mean — The average (mean) value.
From df.describe() now we know :
Maximum value for Occupation is 20 and mean value is around 8.07
The maximum purchase amount for the customers is 23961.0 whereas the minimum purchase amount is 12.0
Measure the variability of data of the dataset
Variability describes how far apart data points lie from each other and from the center of a distribution.
1. Standard Deviation
The standard deviation is the average amount of variability in your dataset.
It tells you, on average, how far each data point lies from the mean. The larger the standard deviation, the more variable the data set is and if zero variance then there is no variability in the dataset that means there no use of that dataset.
So, it helps in understanding the measurements when the data is distributed. The more the data is distributed, the greater will be the standard deviation of that data.Here, you as an individual can determine which company is beneficial in long term. But, if you didn’t know the SD you would have choosen a wrong compnay for you.
df.std()
User_ID 1727.591586
Occupation 6.522660
Marital_Status 0.491770
Product_Category_1 3.936211
Product_Category_2 5.086590
Product_Category_3 4.125338
Purchase 5023.065394
dtype: float64
We can also understand the standard deviation using the below function.
def std_cal(df,float64_lst):
cols = ['normal_value', 'zero_value']
zero_value = 0
normal_value = 0
for value in float64_lst:
rs = round(df[value].std(),6)
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
std_total_df = pd.DataFrame([[normal_value, zero_value]], columns=cols)
return std_total_df
int64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)
std_cal(df,int64_lst)
float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)
std_cal(df,float64_lst)
zero_value -> is the zero variance and when then there is no variability in the dataset that means there no use of that dataset.
2. Variance
The variance is the average of squared deviations from the mean. A deviation from the mean is how far a score lies from the mean.
Variance is the square of the standard deviation. This means that the units of variance are much larger than those of a typical value of a data set.
Why do we used Variance ?
By Squairng the number we get non-negative computation i.e. Disperson cannot be negative. The presence of variance is very important in your dataset because this will allow the model to learn about the different patterns hidden in the data
df.var()
User_ID 2.984573e+06
Occupation 4.254510e+01
Marital_Status 2.418379e-01
Product_Category_1 1.549376e+01
Product_Category_2 2.587339e+01
Product_Category_3 1.701841e+01
Purchase 2.523119e+07
dtype: float64
We can also understand the Variance using the below function.
zero_cols = []
def var_cal(df,float64_lst):
cols = ['normal_value', 'zero_value']
zero_value = 0
normal_value = 0
for value in float64_lst:
rs = round(df[value].var(),6)
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
zero_cols.append(value)
var_total_df = pd.DataFrame([[normal_value, zero_value]], columns=cols)
return var_total_df
var_cal(df, int64_lst)
var_cal(df, float64_lst)
zero_value -> Zero variance means that there is no difference in the data values, which means that they are all the same.
Measure central tendency
A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics.
Null or nan values shows that some users do not buy products from all category.
Another way to remove null and nan values is to use the method “df.dropna(inplace=True)”.
df.dropna(inplace=True)
df.shape
(166821, 12)
Count of unique occurences of every value in all categorical value
objects_cols = ['object']
objects_lst = list(df.select_dtypes(include=objects_cols).columns)
for value in objects_lst:
print(f"{value:{10}} {df[value].value_counts()}")
Product_ID P00025442 1615
P00110742 1612
P00112142 1562
P00057642 1470
P00184942 1440
...
P00296842 2
P00243042 2
P00027842 1
P00060842 1
P00057842 1
Name: Product_ID, Length: 528, dtype: int64
Gender M 129227
F 37594
Name: Gender, dtype: int64
Age 26-35 66942
36-45 33285
18-25 31316
46-50 13374
51-55 11166
55+ 5865
0-17 4873
Name: Age, dtype: int64
City_Category B 69243
C 56059
A 41519
Name: City_Category, dtype: int64
Stay_In_Current_City_Years 1 58287
2 31515
3 29268
4+ 25362
0 22389
Name: Stay_In_Current_City_Years, dtype: int64
Categorical data are variables that contain label values rather than numeric values.The number of possible values is often limited to a fixed set.
Use Label Encoder to label the categorical data. Label Encoder is the part of SciKit Learn library in Python and used to convert categorical data, or text data, into numbers, which our predictive models can better understand.
Label Encoding refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.
#Before Encoding
for value in objects_lst:
print(value)
print()
print(df[value])
print("------------------------------------------------")
print()
Product_ID
1 P00248942
6 P00184942
13 P00145042
14 P00231342
16 P0096642
...
545902 P00064042
545904 P00081142
545907 P00277642
545908 P00127642
545914 P00217442
Name: Product_ID, Length: 166821, dtype: object
------------------------------------------------
Gender
1 F
6 M
13 M
14 F
16 F
..
545902 F
545904 M
545907 M
545908 M
545914 M
Name: Gender, Length: 166821, dtype: object
------------------------------------------------
Age
1 0-17
6 46-50
13 26-35
14 51-55
16 51-55
...
545902 46-50
545904 26-35
545907 26-35
545908 26-35
545914 26-35
Name: Age, Length: 166821, dtype: object
------------------------------------------------
City_Category
1 A
6 B
13 A
14 A
16 A
..
545902 B
545904 B
545907 B
545908 B
545914 B
Name: City_Category, Length: 166821, dtype: object
------------------------------------------------
Stay_In_Current_City_Years
1 2
6 2
13 1
14 1
16 1
..
545902 4+
545904 2
545907 2
545908 2
545914 2
Name: Stay_In_Current_City_Years, Length: 166821, dtype: object
------------------------------------------------
#Encoding categorical data values
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for value in objects_lst:
df[value] = le.fit_transform(df[value])
#After encoding or converting categorical col values into numbers
for value in objects_lst:
print(value)
print()
print(df[value])
print("------------------------------------------------")
print()
Product_ID
1 394
6 287
13 214
14 366
16 520
...
545902 101
545904 120
545907 421
545908 184
545914 346
Name: Product_ID, Length: 166821, dtype: int32
------------------------------------------------
Gender
1 0
6 1
13 1
14 0
16 0
..
545902 0
545904 1
545907 1
545908 1
545914 1
Name: Gender, Length: 166821, dtype: int32
------------------------------------------------
Age
1 0
6 4
13 2
14 5
16 5
..
545902 4
545904 2
545907 2
545908 2
545914 2
Name: Age, Length: 166821, dtype: int32
------------------------------------------------
City_Category
1 0
6 1
13 0
14 0
16 0
..
545902 1
545904 1
545907 1
545908 1
545914 1
Name: City_Category, Length: 166821, dtype: int32
------------------------------------------------
Stay_In_Current_City_Years
1 2
6 2
13 1
14 1
16 1
..
545902 4
545904 2
545907 2
545908 2
545914 2
Name: Stay_In_Current_City_Years, Length: 166821, dtype: int32
------------------------------------------------
Skewness
Skewness is a measure of the asymmetry of a distribution. A distribution is asymmetrical when its left and right side are not mirror images. A distribution can have right (or positive), left (or negative), or zero skewness
Why do we calculate Skewness ?
Skewness gives the direction of the outliers if it is right-skewed, most of the outliers are present on the right side of the distribution while if it is left-skewed, most of the outliers will present on the left side of the distribution
Below is the function to calculate skewness.
def right_nor_left(df, int64_lst):
temp_skewness = ['column', 'skewness_value', 'skewness (+ve or -ve)']
temp_skewness_values = []
temp_total = ["positive (+ve) skewed", "normal distrbution" , "negative (-ve) skewed"]
positive = 0
negative = 0
normal = 0
for value in int64_lst:
rs = round(df[value].skew(),4)
if rs > 0:
temp_skewness_values.append([value,rs , "positive (+ve) skewed"])
positive = positive + 1
elif rs == 0:
temp_skewness_values.append([value,rs,"normal distrbution"])
normal = normal + 1
elif rs < 0:
temp_skewness_values.append([value,rs, "negative (-ve) skewed"])
negative = negative + 1
skewness_df = pd.DataFrame(temp_skewness_values, columns=temp_skewness)
skewness_total_df = pd.DataFrame([[positive, normal, negative]], columns=temp_total)
return skewness_df, skewness_total_df
float64_cols = ['float64']
float64_lst_col = list(df.select_dtypes(include=float64_cols).columns)
skew_df,skew_total_df = right_nor_left(df, float64_lst_col)
skew_df
skew_total_df
int64_cols = ['int64','int32']
int64_lst_col = list(df.select_dtypes(include=int64_cols).columns)
skew_df,skew_total_df = right_nor_left(df, int64_lst_col)
skew_df
skew_total_df
We notice with the above results that we have following details:
7 columns are positive skewed
5 columns are negative skewed
Step 3 Insights: -
With the statistical analysis we have found that the data have a lot of skewness in them all the columns are positively skewed with mostly zero variance.
Statistical analysis is little difficult to understand at one glance so to make it more understandable we will perform visulatization on the data which will help us to understand the process easily.
Why we are calculating all these metrics?
Mean / Median /Mode/ Variance /Standard Deviation are all very basic but very important concept of statistics used in data science. Almost all the machine learning algorithm uses these concepts in data preprocessing steps. These concepts are part of descriptive statistics where we basically used to describe and understand the data for features in Machine learning
Black Friday Benefits
Retailers may spend an entire year planning their Black Friday sales. They use the day as an opportunity to offer rock-bottom prices on overstock inventory and to offer doorbusters and discounts on seasonal items, such as holiday decorations and typical holiday gifts. Benefits of black friday sales: -
Increases traffic, sales, and revenue.
Customer acquisition.
Establishes brand presence.
Step 4: Data Exploration
Goal/Purpose:
Graphs we are going to develop in this step
Histogram of all columns to check the distrubution of the columns
Distplot or distribution plot of all columns to check the variation in the data distribution
Heatmap to calculate correlation within feature variables
Boxplot to find out outlier in the feature columns
Scatter Plot to show the relation between variables
1. Histogram
A histogram is a bar graph-like representation of data that buckets a range of classes into columns along the horizontal x-axis.The vertical y-axis represents the number count or percentage of occurrences in the data for each column
# Distribution in attributes
%matplotlib inline
import matplotlib.pyplot as plt
df.hist(bins=50, figsize=(30,30))
plt.show()
Histogram Insight: -
Histogram helps in identifying the following:
View the shape of your data set’s distribution to look for outliers or other significant data points.
Determine whether something significant has boccurred from one time period to another.
From the above histogram we observe that the Customers whose occupations are 0 and 4, city category B, and the Age group (26–35) makes the most no. of purchases during Black Friday Sales.
Male customers have done more transactions than female during Black Friday sales and there are more unmarried customers in the dataset who purchase more during Black Friday sales.
Why Histogram?
It is used to illustrate the major features of the distribution of the data in a convenient form. It is also useful when dealing with large data sets (greater than 100 observations). It can help detect any unusual observations (outliers) or any gaps in the data.
From the above graphical representation we can identify that the highest bar represents the outliers which is above the maximum range.
We can also identify that the values are moving on the right side, which determines positive and the centered values determines normal skewness.
2. Distplot
A Distplot or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables. The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it
num = [f for f in df.columns if df.dtypes[f] != 'object']
nd = pd.melt(df, value_vars = num)
n1 = sns.FacetGrid (nd, col='variable', col_wrap=4, sharex=False, sharey = False)
n1 = n1.map(sns.distplot, 'value')
n1
<seaborn.axisgrid.FacetGrid at 0x22387306650>
Distplot Insights: -
Above is the distrution bar graphs to confirm about the statistics of the data about the skewness, the above results are:
7 columns are positive skewed and 5 columns are negative skewed.
1 column is added here i.e count which is our target variable ~ which is also -ve skewed. In that case we’ll need to cube root transform this variable so that it becomes normally distributed. A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables
Why Distplot?
Skewness is demonstrated on a bell curve when data points are not distributed symmetrically to the left and right sides of the median on a bell curve. If the bell curve is shifted to the left or the right, it is said to be skewed.
We can observe that the bell curve is shifted to left we indicates positive skewness.As all the column are positively skewed we don’t need to do scaling.
Let’s proceed and check the distribution of the target variable.
#+ve skewed
df['Purchase'].skew()
-0.08458767367814019
The target variable is negatively skewed.A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables.
3. Heatmap
A heatmap (or heat map) is a graphical representation of data where values are depicted by color.Heatmaps make it easy to visualize complex data and understand it at a glance
Correlation — A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, when one variable increases as the other variable increases, or one variable decreases while the other decreases.
Correlation can have a value:
1 is a perfect positive correlation
0 is no correlation (the values don’t seem linked at all)
-1 is a perfect negative correlation
#correlation plot
sns.set(rc = {'figure.figsize':(12,12)})
corr = df.corr().abs()
sns.heatmap(corr,annot=True)
plt.show()
Notice the last column from right side of this map. We can see the correlation of all variables against Purchase . As you can see, some variables seem to be strongly correlated with the target variable. Here, a numeric correlation score will help us understand the graph better.
print (corr['Purchase'].sort_values(ascending=False)[:15], '\n') #top 15 values
print ('----------------------------------------')
print (corr['Purchase'].sort_values(ascending=False)[-5:]) #last 5 values`
Purchase 1.000000
Product_Category_1 0.396558
Product_Category_2 0.153711
Product_ID 0.108375
City_Category 0.077344
Gender 0.060852
Occupation 0.025048
Age 0.023937
Product_Category_3 0.022006
Stay_In_Current_City_Years 0.007598
Marital_Status 0.004603
User_ID 0.000590
Name: Purchase, dtype: float64
----------------------------------------
Age 0.023937
Product_Category_3 0.022006
Stay_In_Current_City_Years 0.007598
Marital_Status 0.004603
User_ID 0.000590
Name: Purchase, dtype: float64
Here we see that the Product_Category_1 feature is 39% correlated with the target variable.
This shows that more customers buy products from category 1.
corr
Heatmap insights: -
As we know, it is recommended to avoid correlated features in your dataset. Indeed, a group of highly correlated features will not bring additional information (or just very few), but will increase the complexity of the algorithm, hence increasing the risk of errors.
Why Heatmap?
Heatmaps are used to show relationships between two variables, one plotted on each axis. By observing how cell colors change across each axis, you can observe if there are any patterns in value for one or both variables.
4. Boxplot
A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile [Q1], median, third quartile [Q3] and “maximum”).
Basically, to find the outlier in a dataset/column.
features = df.columns.tolist()
features.remove('Purchase')
sns.boxplot(data=df)
<Axes: >
The dark points are known as Outliers. Outliers are those data points that are significantly different from the rest of the dataset. They are often abnormal observations that skew the data distribution, and arise due to inconsistent data entry, or erroneous observations.
Boxplot Insights: -
Sometimes outliers may be an error in the data and should be removed. In this case these points are correct readings yet they are different from the other points that they appear to be incorrect.
The best way to decide wether to remove them or not is to train models with and without these data points and compare their validation accuracy.
So we will keep it unchanged as it won’t affect our model.
Here, we can see that most of the variables possess outlier values. It would take us days if we start treating these outlier values one by one. Hence, for now we’ll leave them as is and let our algorithm deal with them. As we know, tree-based algorithms are usually robust to outliers.
Why Boxplot?
Box plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. They are built to provide high-level information at a glance, offering general information about a group of data’s symmetry, skew, variance, and outliers.
In the next step we will divide our cleaned data into training data and testing data.
Step 2: Data Preparation
Goal:-
Tasks we are going to in this step:
Now we will spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
Split dataset into train and test dataset.
Scaling on train dataset.
1. Now we spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
# Separate target and feature column in X and y variable
target = 'Purchase'
# X will be the features
X = df.drop(target,axis=1)
#y will be the target variable
y = df[target]
y have target variable and X have all other variable.
Here in black friday sales prediction, Purchase is the target variable.
X.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 166821 entries, 1 to 545914
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 User_ID 166821 non-null int64
1 Product_ID 166821 non-null int32
2 Gender 166821 non-null int32
3 Age 166821 non-null int32
4 Occupation 166821 non-null int64
5 City_Category 166821 non-null int32
6 Stay_In_Current_City_Years 166821 non-null int32
7 Marital_Status 166821 non-null int64
8 Product_Category_1 166821 non-null int64
9 Product_Category_2 166821 non-null float64
10 Product_Category_3 166821 non-null float64
dtypes: float64(2), int32(5), int64(4)
memory usage: 12.1 MB
y
1 15200
6 19215
13 15665
14 5378
16 13055
...
545902 8047
545904 16493
545907 3425
545908 15694
545914 11640
Name: Purchase, Length: 166821, dtype: int64
# Check the shape of X and y variable
X.shape, y.shape
((166821, 11), (166821,))
# Reshape the y variable
y = y.values.reshape(-1,1)
# Again check the shape of X and y variable
X.shape, y.shape
((166821, 11), (166821, 1))
2. Spliting the dataset in training and testing data.
Here we are spliting our dataset into 80/20 percentage where 80% dataset goes into the training part and 20% goes into testing part.
# Split the X and y into X_train, X_test, y_train, y_test variables with 80-20% split.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Check shape of the splitted variables
X_train.shape, X_test.shape, y_train.shape, y_test.shape
((133456, 11), (33365, 11), (133456, 1), (33365, 1))
Insights: -
Train test split technique is used to estimate the performance of machine learning algorithms which are used to make predictions on data not used to train the model.It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small dataset and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced.
In the next step we will train our model on the basis of our training and testing data.
Step 3: Model Training
Goal:
In this step we are going to train our dataset on different regression algorithms. As we know that our target variable is not discrete format so we have to apply regression algorithm. In our dataset we have the outcome variable or Dependent variable i.e Y having non discrete values. So we will use Regression algorithm.
Algorithms we are going to use in this step
Linear Regression
Lasso Regression
Ridge Regression
RandomForestRegressor
K-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset. There are commonly used variations on cross-validation, such as stratified and repeated, that are available in scikit-learn
# Define kfold with 10 split
cv = KFold(n_splits=10, shuffle=True, random_state=42)
The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).
1. Linear Regression
Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables.
Train set cross-validation
#Using Linear Regression Algorithm to the Training Set
from sklearn.linear_model import LinearRegression
li_R = LinearRegression() #Object Creation
li_R.fit(X_train, y_train)
LinearRegression()
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LinearRegression
LinearRegression()
#Accuracy check of trainig data
#Get R2 score
li_R.score(X_train, y_train)
0.1789891904409492
#Accuracy of test data
li_R.score(X_test, y_test)
0.18093544538535944
# Getting kfold values
li_scores = -1 * cross_val_score(li_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
li_scores
array([4604.45248666, 4582.73001938, 4646.76348762, 4619.53087245,
4595.74770955, 4558.83488463, 4558.74395781, 4586.0313374 ,
4618.82565705, 4638.70018072])
# Mean of the train kfold scores
li_score_train = np.mean(li_scores)
li_score_train
4601.0360593260075
Prediction
Now we will perform prediction on the dataset using Linear Regression.
# Predict the values on X_test_scaled dataset
y_predicted = li_R.predict(X_test)
Various parameters are calculated for analysing the predictions.
- Confusion Matrix 2)Classification Report 3)Accuracy Score 4)Precision Score 5)Recall Score 6)F1 Score
Confusion Matrix
A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes. It plots a table of all the predicted and actual values of a classifier.
This diagram helps in understanding the concept of confusion matrix.
Evaluating all kinds of evaluating parameters.
Classification Report :
A classification report is a performance evaluation metric in machine learning. It is used to show the precision, recall, F1 Score, and support of your trained classification model.
F1_score :
The F1 score is a machine learning metric that can be used in classification models.
Precision_score :
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0.
Recall_score :
Recall score is used to measure the model performance in terms of measuring the count of true positives in a correct manner out of all the actual positive values. Precision-Recall score is a useful measure of success of prediction when the classes are very imbalanced.
# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
from sklearn.metrics import r2_score
li_acc = r2_score(y_test, y_predicted)*100
print("The model used is Linear Regression")
print("R2 Score is: -")
print()
print(li_acc)
The model used is Linear Regression
R2 Score is: -
18.093544538535944
2. Lasso Regression
Lasso regression is also called Penalized regression method. This method is usually used in machine learning for the selection of the subset of variables. It provides greater prediction accuracy as compared to other regression models. Lasso Regularization helps to increase model interpretation.
#Using Lasso Regression
from sklearn import linear_model
la_R = linear_model.Lasso(alpha=0.1)
#looking for training data
la_R.fit(X_train,y_train)
Lasso(alpha=0.1)
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Lasso
Lasso(alpha=0.1)
#Accuracy check of trainig data
la_R.score(X_train, y_train)
0.1789891848908156
# Getting kfold values
la_scores = -1 * cross_val_score(la_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
la_scores
array([4604.45072579, 4582.72888841, 4646.76186425, 4619.53042965,
4595.74765443, 4558.83468059, 4558.7410465 , 4586.03634958,
4618.83067311, 4638.6984738 ])
# Mean of the train kfold scores
la_score_train = np.mean(la_scores)
la_score_train
4601.036078610285
Prediction
Now we will perform prediction on the dataset using Lasso Regression.
# Predict the values on X_test_scaled dataset
y_predicted=la_R.predict(X_test)
Evaluating all kinds of evaluating parameters.
#Accuracy check of test data
la_acc = r2_score(y_test,y_predicted)*100
print("The model used is Lasso Regression")
print("R2 Score is: -")
print()
print(la_acc)
The model used is Lasso Regression
R2 Score is: -
18.093596140599043
3. Ridge Regression
Ridge regression is used when there are multiple variables that are highly correlated. It helps to prevent overfitting by penalizing the coefficients of the variables. Ridge regression reduces the overfitting by adding a penalty term to the error function that shrinks the size of the coefficients.
#Using Ridge Regression
from sklearn.linear_model import Ridge
ri_R = Ridge(alpha=1.0)
#looking for training data
ri_R.fit(X_train,y_train)
Ridge()
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Ridge
Ridge()
#Accuracy check of trainig data
ri_R.score(X_train, y_train)
0.17898919043856
# Getting kfold values
ri_scores = -1 * cross_val_score(ri_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
ri_scores
array([4604.45259996, 4582.72999826, 4646.7634481 , 4619.53082031,
4595.74773168, 4558.83471997, 4558.74394234, 4586.0314034 ,
4618.82573828, 4638.7001368 ])
# Mean of the train kfold scores
ri_score_train = np.mean(ri_scores)
ri_score_train
4601.036053909317
Prediction
Now we will perform prediction on the dataset using Ridge Regression.
# Predict the values on X_test_scaled dataset
y_predicted=ri_R.predict(X_test)
Evaluating all kinds of evaluating parameters.
#Accuracy check of test data
ri_acc = r2_score(y_test,y_predicted)*100
print("The model used is Ridge Regression")
print("R2 Score is: -")
print()
print(ri_acc)
The model used is Ridge Regression
R2 Score is: -
18.09354395382796
4. RandomForestRegressor
Random Forest Regression algorithms are a class of Machine Learning algorithms that use the combination of multiple random decision trees each trained on a subset of data. The use of multiple trees gives stability to the algorithm and reduces variance. The random forest regression algorithm is a commonly used model due to its ability to work well for large and most kinds of data.
#Using Logistic Regression Algorithm to the Training Set
from sklearn.ensemble import RandomForestRegressor
rr_R = RandomForestRegressor() #Object Creation
rr_R.fit(X_train, y_train)
RandomForestRegressor()
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RandomForestRegressor
RandomForestRegressor()
#Accuracy check of trainig data
#Get R2 score
rr_R.score(X_train, y_train)
0.939631963567997
#Accuracy of test data
rr_R.score(X_test, y_test)
0.5716231914081544
Prediction
Now we will perform prediction on the dataset using Random Forest Regressor.
# Predict the values on X_test_scaled dataset
y_predicted = rr_R.predict(X_test)
# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
print("The model used is RandomForestRegressor")
rr_acc = r2_score(y_test, y_predicted)*100
print("R2 Score is: -")
print()
print(rr_acc)
The model used is RandomForestRegressor
R2 Score is: -
57.16231914081544
Insight: -
cal_metric=pd.DataFrame([li_acc,la_acc,ri_acc,rr_acc],columns=["Score in percentage"])
cal_metric.index=['Linear Regression',
'Lasso Regression',
'Ridge Regression',
'Random Forest Regressor']
cal_metric
As you can see with our Random Forest Regressor(0.5708 or 57.08%) we are getting a better result.
So we gonna save our model with Random Forest Regressor Algorithm.
Step 4: Save Model
Goal:- In this step we are going to save our model in pickel format file.
import pickle
pickle.dump(li_R , open('black_friday_sales_pred_li.pkl', 'wb'))
pickle.dump(la_R , open('black_friday_sales_pred_la.pkl', 'wb'))
pickle.dump(ri_R , open('black_friday_sales_pred_ri.pkl', 'wb'))
pickle.dump(rr_R , open('black_friday_sales_pred_rr.pkl', 'wb'))
import pickle
def model_prediction(features):
pickled_model = pickle.load(open('black_friday_sales_pred_rr.pkl', 'rb'))
bl = str(pickled_model.predict(features)[0])
return str(f'The black friday sales is {bl}')
We can test our model by giving our own parameters or features to predict.
User_ID = 1004601.0
Product_ID = 42.0
Gender = 1.0
Age = 3.0
Occupation = 14.0
City_Category = 2.0
Stay_In_Current_City_Years = 0.0
Marital_Status = 0.0
Product_Category_1 = 1.0
Product_Category_2 = 8.0
Product_Category_3 = 16.0
model_prediction([[User_ID, Product_ID,Gender, Age, Occupation, City_Category, Stay_In_Current_City_Years, Marital_Status, Product_Category_1, Product_Category_2, Product_Category_3]])
'The black friday sales is 12808.0'
Conclusion
After observing the problem statement we have build an efficient model to solve the problem. The above model helps in predicting the sales of black friday. The accuracy for the prediction is 57.08%.
Checkout whole project codehere(github repo).
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