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👩‍👩‍👦Problem Statement Understanding

For any data Science Problem, we are given the problem statement along with the Data set.

🧐Attributes Description

Now we need to understand the features or attributes of the given dataset in order to understand more about the problem

Example of Attributes/Features -

Variable	Description
Variable	Definition
ID	Unique ID
Gender	Gender of the customer
Ever_Married	Marital status of the customer
Age	Age of the customer
Graduated	Is the customer a graduate?
Profession	Profession of the customer
Work_Experience	Work Experience in years
Spending_Score	Spending score of the customer
Family_Size	Number of family members for the customer (including the customer)
Var_1	Anonymised Category for the customer

📌Concepts and Techniques used in the Project

🪁Data Wrangling

Data wrangling is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. This includes -

• Basic Cleaning
• Getting information about attributes
• Data manipulation
• Organising of Data

🪁Missing Values Treatment

If the missing data is Present in -

• Continuous variable feature - Fill Median or mean based on the distriution of feature variale
• Categorical Variable feature - Fill mode of the column in place of missing data

🪁Exploratory Data Analysis

1. Variable Identification

• Categorical
  • Ordinal
  • Nominal
• Continuous

2. Univariate Analysis

• For Categorical Variable -
  • Count of data present in the dataset for a particular variable
• For Continuous Variable -
  • Find the Distribution of features using Histogram
  • Outlier detection using Box Plot

3. Bi-Variate Analysis

• Categorical - Continuous Variables ---> Bar Graph
• Continuous - Continuous Variables ---> Scatter Plot to see relationship

4. Outlier Detection

• Box plots are the best statistical Measure for Outlier detection

5. Missing value Treatment

• Depending upon the dataset the missing value treatment can be done before EDA or after EDA process

🪁Data Preprocessing

1. Feature Engineering

• After analysing the dataset and based on the insights we add or remove some attributes so that our model performs good.

2. Feature Encoding

• Since the Machine learning model accepts only Numerical values so the categorical variables which have 'object' data types are converted into numerical values depending upon the type of variable
  • Ordinal Categorical variable - Label Encoding
  • Nominal Categorical Variable - One Hot Encoding

3. Feature Scaling

• Features are scaled to give the good output for some algorithms which depends on some similiarty function like -
  • K-Means Clustering algorithm
  • K-NN algorithm
  • Principal Component Analysis

✂Machine Learning Model Building

Now we select the Machine learning algorithm which can give the best model outcome predictions