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.

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

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".

Exploratory Data Analysis Part-1

Credit Card Approval Prediction based on users' historic data.

Customer churn prediction using deep learning

Performing kmeans clustering and also providing elbow plot

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

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

RFM analysis focuses on identifying and segmenting customers based on their purchasing behavior. Analyzed to understand and interact with customers. It can be used together for more effective marketing and customer management strategies.

Unsupervised machine learning models used to group the cryptocurrencies to help prepare for a new investment.

A small scaling algorithm for integer sequences.

A Book Recommendation System that utilizes Python libraries such as numpy, pandas, seaborn, and matplotlib to recommend books based on user input.

[ Analyzing the existing customer data and getting valuable insights about the purchase pattern ] | K-Means clustering | silhouette score | minmaxscalar |

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.