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This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc

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sayaliwalke30/Kaggle-Projects

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Kaggle Machine Learning Projects

/**

author Sayali Walke

**/

This repository contains following projects:

1] House Price Prediction (Jan 2019- Feb 2019)

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015 and our task is to build a machine learning model that can predict the house prices.

2] Credit card Fraud detection (Feb 2019 - March 2019)

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. So the goal of this project is to identify fraudulent credit card transactions

3] Bank loan Prediction (March 2019 - April 2019)

When a customer applies for a loan, banks and other credit providers use statistical models to determine whether or not to grant the loan based on the likelihood of the loan being repaid.

4] Diabetes Prediction (May 2019 - June 2019)

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.

This repository will demonstrate following skills-

1] Exploratory Data Analysis.

2] Data Cleaning

3] Data Munging / Transformation

4] Feature Selection and Feature Engineering

5] Machine Learning models including Linear regression, Polynomial regression,

Logistic regression, Descision Tree, Random Forest, Lasso & Ridge regression to avoid overfitting

6] Compared the performance using various accuracy metrics.

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This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc

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