Customer churn prediction using deep learning

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

Credit Card Approval Prediction based on users' historic data.

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.

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

Analyzing and predicting the stock prices,multiple machine learning models, including LSTM (Long Short-Term Memory), Prophet, and others

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.

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

A small scaling algorithm for integer sequences.

An innovative system for filtering and categorizing movie reviews

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.

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