Feature Engineering with Python

This file provides full practice of data preprocessing methods and techniques using different types of libraries.

There are lot of things that need to be done on the given dataset before we feed it to the machine, these things come under data preprocessing. In this repository I have tried to explain those things with some examples.

The Bike Sharing Company wants to understand the independent variables on their past data to analyze and create a machine learning model to understand the demand of the bike and accordingly plan a business strategy.

X Education Organization wants to identify if a customer registered on their website for enquiry is a potential customer or not. Using past data to build a machine learning algorithm

different types of regression

To predict which customer is most likely to convert

This python code shows howw regression is handled in case of categorical variables using duumies. It calculates the multiple regression code and shows the regression table. It also performs the residual analysis.

Introduction to Machine Learning course - Spring 2021 - Supervised and Unsupervised Learning, KNN Classification Models, Naive-Bayes Classifier, Regression Analysis, K-Means and DBSCAN Clustering Analysis, Association Rules and PCA, Confusion Matrix, Normalization, Dummy Variables.

Predictive model that tells important factors(or features) affecting the demand for shared bikes

Sample programs with basic machine learning concepts

Scientific programming through the SKLearn / Scikitlearn library

Working Examples of all algorithms with datasets

Business Goal: To model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations.

King County Real Estate Model

Build a model with machine learning to predict housing prices in Ames, Iowa. Top 11% in the Kaggle Housing Prices Competition.

Goal is to predict the miles per gallon of the cars using different attributes

Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Com…