This repository showcase my projects.
You've been hired as a Data Scientist to solve one business usecase in FMCG company wherein you have been given fictitious dataset of Customers who buy products in retail store. The task is to segment the customers and find the purchase, brand choice and purchase quantity probabilities.
Python NoteBook: Customer and Purchase Analytics
We are given data from an Audiobook app. Logically, it relates only to the audio versions of books. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.
The main idea is that if a customer has a low probability of coming back, there is no reason to spend any money on advertizing to him/her. If we can focus our efforts ONLY on customers that are likely to convert again, we can make great savings. Moreover, this model can identify the most important metrics for a customer to come back again. Identifying new customers creates value and growth opportunities.
Create a machine learning algorithm, which is able to predict if a customer will buy again.
Python NoteBook: Audiobooks Business Usecase
Objective : The goal is to predict whether the customer will renew the job slot product subscription or not.
Python Notebook: Glassdoor Analytics Use Case
ML-Model-Flask-Deployment : https://job-slot-retention.herokuapp.com/
The goal of the project is to to predict possibly interesting subreddits to a user based on their comment history. The hypothesis of the recommender model is, given an ordered sequence of user subreddit interactions, patterns will emerge that favour the discovery of paticular new subreddits given that historical user interaction sequence.
Implemented three models :
- Simple Popularity Based Recommender System.
- Item-Item Based Collaborative Filtering Recommender System.
- User Based Collaborative Filtering Recommender System
Python Notebook: Subreddit Recommendor System
The goal of the project was to detect the user intent and classify the user chat into the following categories - food,recharge,support,reminders,travel,nearby,movies,casual and other.
A chat can be classified into multiple categories Ex. “Are there any offers going on?” This query could belong to all the domains in which transactions are possible ( travel, recharge, food, movies, home services, etc) Thus it was a Multiclass and multilabel problem.
- Implemented a multi-label classifier using the training data. The classifier tagged all the possible domains (food, support etc) for each query using Decision Tree, Logistic Regression and Random forest algorithms.
- Achieved a label accuracy of 94% and subset accuracy of 73%
Python Notebook: Haptik NLP Project
Content: This data set contains booking information for a city hotel and a resort hotel and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.
Objective: To predict whether the guest would actually come or not.
Dataset Source: https://www.kaggle.com/jessemostipak/hotel-booking-demand
Python Notebook: EDA Hotel Booking Project