ML / DL Algorithms implemented from scratch. Developed with only numpy as dependency. Machine Learning Algorithms such as Support Vector Machine, Linear Regression, Artificial Neural Networks and other data transformation algorithms are implemented. Project is released as a python package and can be download from Python Package Installer.

I have created this project as a part of virtual internship programme in data Science.

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

An innovative system for filtering and categorizing movie reviews

Processing of data gaps, coding of categorical features, data scaling.

Exploratory Data Analysis Part-1

The sixth project from a Data Scientist with Python track by DataCamp

This is a project to detect anomalies in pump sensor data using One-Class Support Vector Machines (SVM). The data is preprocessed by dropping columns with missing values and scaled using MinMaxScaler. The one-class SVM classifier is trained and used to predict anomalies in the data, which are then saved in a new file "results.csv".

Credit Card Approval Prediction based on users' historic data.

Customer churn prediction using deep learning

This project predicts whether a person survived the Titanic disaster based on various features using machine learning. It utilizes pipelines, ColumnTransformer, and model serialization for efficient processing and prediction.

Performing kmeans clustering and also providing elbow plot

Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).

pipelines chains together multiple steps so that the output of each step is used as input to the next step

The main objective of this project is to design and implement a robust data preprocessing system that addresses common challenges such as missing values, outliers, inconsistent formatting, and noise. By performing effective data preprocessing, the project aims to enhance the quality, reliability, and usefulness of the data for machine learning.

Utilizing San Francisco crime data to identify hotspots using K-means clustering techniques.

The Bike Sharing Company wants to understand the independent variables on their past data to analyze and create a machine learning model to understand the demand of the bike and accordingly plan a business strategy.

Anomaly Detection Using Gaussian Mixture Model