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advanced-regression-using-lasso-and-ridge

Car price prediction using Lasso and Ridge

books

Free Ebooks

car-price-prediction-linear-regression

This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of car prices.

credit-card-fraud-detection

The main objective of this case study is to detect if the credit card transactions are fraudulent or not. To determine how these frauds are affecting the bank’s business. We would use Machine Learning models to achieve this.

data_science_questions

eda-casestudy

This case study aims to give you an idea of applying EDA in a real business scenario. In this case study, apart from applying the techniques that you have learnt in the EDA module, you will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.

eda1

gdp-analysis

GDP Analysis of Indian States

house-price-prediction-ridge-and-lasso-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. Business Goal You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

lead-scoring-case-study

Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads.

ml-for-beginners

12 weeks, 24 lessons, classic Machine Learning for all

open-source-spotlight

Introductory notebooks used in my videos, covering great open-source Python packages.

pca-clustering

Categorize the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most.

pgdds-projects

Post Graduate Diploma in Data Science Projects

powerbi-jupyter

A Custom Jupyter Widget Library for Power BI

python-for-data-science-for-dummies

Python 3 code for Python for Data Science For Dummies by Luca Massaron and John Paul Mueller

siim-isic-melanoma-classification

Identify melanoma in lesion images

stock-market-analysis--mysql

Problem Introduction: Two of India's biggest stock exchanges BSE and NSE, collectively clear trades combining to greater than 40,000 crores every day. As you might already be aware, a lot of trading happens on the basis of technical and fundamental analysis.

telecom-churn-case-study

Problem Statement: In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

telecom_churn_model

Analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

the-kaggle-workbook

Code Repository for The Kaggle Workbook, Published by Packt

upgrad

Pyspark Demo Notebooks