This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

Forecasting Netflix Customer Retention based on Gaussian Process Regression

I have built a Model using the Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

A repo packed with common and important machine learning techniques and algorithm implementations using sklearn.

Using a dataset provided by Airbnb, analysis and predictions will be made to understand what effects the total price of an Airbnb

Disaster response project that implements data engineering tactics to classify messages sent during a real-world disaster. This project uses ETL and ML pipelines and uses the Flask library to deploy the final result on a website.

A machine learning model built to predict if a credit card application will get approved.

Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.

Analyze and Build a machine learning (ML) model on the Iris Flower dataset

RTA severity predictor is an application which predicts the severity of road traffic accident, so as to pave the way for improving the safety level of road traffic.

Building Machine Learning and ETL Pipelines to categorize emergency messages based on the needs communicated by the sender

Use decision trees to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

Diabetes Prediction with Tree based models (Random Forest and XGBoost). Grid Search CV and Randomized Search CV used to optimize parameters

The "Potential Customers Prediction" project uses machine learning to predict which customers are likely to make a purchase. It involves data cleaning, exploratory analysis, and building models to identify potential buyers based on demographics and behavior. The model's performance is evaluated using metrics like accuracy and precision.

Learning Machine Learning Through Data

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.

Prepare a model for glass classification using KNN and Implement a KNN model to classify the animals in to categorie.

Implented ridge and lasso regression by understanding the use of parameters