Data Science Portfolio

Advanced Linear Regression

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not. The company wants to know: Which variables are significant in predicting the price of a house, and How well those variables describe the price of a house. Also, determine the optimal value of lambda for ridge and lasso regression.

Steps Involved:

Importing modules, Reading the data

Analyzing Numerical Features

Outlier Treatement

Correlation Analysis

Missing value treatement

Univariate , Bivariate Analysis

Data Visualization

Encoding Categorical Features

Splitting data into Train and Test data

Transformation of Target Variable

Feature Scaling

Primary Feature Selection using RFE

Ridge Regression

Lasso Regression

Comparing model coefficients

Model Evaluation

Choosing the final model and most significant features.

Linear Regression - Boom Bikes Dataset

This Project is based on the rental bikes data set from a company called Boom Bikes.

Work Flow

Data Loading and Understanding

Data Visualization

Data Preparation - Split into test,train and Rescale

Data Modelling

Residual Analysis, Checking Assumptions of Linear Regression

Prediction and Evaluation on the Test Dataset

EDA - Lending Club Case Study

This case study is based on Lending Club Dataset. Based on the available data set, we are required to draw insights which help in categorizing a new customer. The objective of this case study is to analyze the data to identify customers who might default the loan.

Table of Contents

Data Cleaning

Data Standardization

Missing value treatment

Outliers Check

Univariate Analysis and Segmented Univariate Analysis

Observations from Univariate and Segmented Univariate Analysis

Bivariate or Multivariate Analysis

Observations from Bivariate or Multivariate Analysis

Malicious URL Detection

The aim of this project is to classify a URL as a Malicious or Benign URL. Collected a public dataset that consists of 450176 rows and two classes of URLs: Malicious or Benign. Have extracted a total of 17 features which consist of Lexical features, count-based features, and two binary features. Trained the model on Adaboost and Random Forest classifiers and used the Voting Classifier ensembling method to get the optimum result among the two classifiers

Data Collection
Pre Processing
Balancing the dataset
Split the Dataset into train, test, and validation
Model Training
Model Evaluation

This Project submitted as a thesis at GITAM University, Hyderabad.

Authors: Srinath Tummala, Shivani Donthi