Always know what to expect from your data.

My Streamlit Web App in which I show the findings of analyzing all my chess.com games 😮 Analyze your own games by just providing the username 😍

Data-Acquisition and Basic Insights, Data Wrangling, Exploratory Data Analysis (EDA), and Training Prediction Models(Machine Learning) on two datasets.

Exploratory Dataset Analysis (EDA) will be uploaded to this repository. Libraries such as Pandas, Matplotlib, Seaborn and Plotly will be used for data analysis.

Utilizing Exploratory Data Analysis (EDA) and feature engineering in Python to enhance data quality and uncover valuable insights.

This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

Data Analysis on various Datasets

Code for classifying breast cancer tumors using machine learning. Includes preprocessing, visualizations, and models like Logistic Regression, Decision Tree, and Random Forest. Evaluated with accuracy, precision, recall, and F1-score. Clone, install dependencies, and run the Jupyter notebook for full analysis.

The goal of this project is to predict the Remaining Useful Life (RUL) of aircraft engines based on sensor data. Predictive maintenance helps identify the point at which an engine is likely to fail, allowing for timely maintenance to prevent failures and optimize maintenance schedules.

The project uses data preprocessing steps, such as handling missing values, encoding categorical variables, and standardizing features. It applies the K-Means clustering algorithm and visualizes the results using various libraries like Matplotlib, Seaborn, and Plotly.

EDA and hypothesis testing for customer acquisition through marketing campaigns.

a web application for analyzing data and viewing meaningful insights.

This repository contains a collection of data science projects which I did during the IBM Data Science Professional certification programme. Each project demonstrates different aspects of data science, data analysis, data visualization and machine learning.

NLP based Twitter data sentiment analysis project

This project involves time series analysis, examining Tesla Inc.'s (TSLA) stock performance from 2010 to 2024. Time series analysis, which studies data points collected at specific time intervals, was used to identify trends, seasonal patterns & fluctuations in Tesla’s stock prices, insights were derived from visualizations & resampling techniques.

The repo focuses on my works in data science

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Exploratory data analysis in Python of the quasar candidates catalog by Richards et al., ApJS 219 (2015).

Machine learning from scratch. All the codes have been written.

Employing Different Machine Learning Models to Detect Credit Card Fraud.