Harshad Maurya's starred repositories
best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
Statistics-Cheatcode
For Data Enthusiasts Statistics Cheatsheet 2024
E-Commerce-Machine-learning-and-NLP-Techniques-used-
Problem Statement Amazon is an online shopping website that now caters to millions of people everywhere. Over 34,000 consumer reviews for Amazon brand products like Kindle, Fire TV Stick and more are provided. The dataset has attributes like brand, categories, primary categories, reviews.title, reviews.text, and the sentiment. Sentiment is a categorical variable with three levels "Positive", "Negative“, and "Neutral". For a given unseen data, the sentiment needs to be predicted. You are required to predict Sentiment or Satisfaction of a purchase based on multiple features and review text. picture Dataset Snapshot picture Project Task: Week 1 Class Imbalance Problem: Perform an EDA on the dataset. a) See what a positive, negative, and neutral review looks like. b) Check the class count for each class. It’s a class imbalance problem. Convert the reviews in Tf-Idf score. Run multinomial Naive Bayes classifier. Everything will be classified as positive because of the class imbalance. Project Task: Week 2 Tackling Class Imbalance Problem: Oversampling or undersampling can be used to tackle the class imbalance problem. In case of class imbalance criteria, use the following metrices for evaluating model performance: precision, recall, F1-score, AUC-ROC curve. Use F1-Score as the evaluation criteria for this project. Use Tree-based classifiers like Random Forest and XGBoost. Note: Tree-based classifiers work on two ideologies namely, Bagging or Boosting and have fine-tuning parameter which takes care of the imbalanced class. Project Task: Week 3 Model Selection: Apply multi-class SVM’s and neural nets. Use possible ensemble techniques like: XGboost + oversampled_multinomial_NB. Assign a score to the sentence sentiment (engineer a feature called sentiment score). Use this engineered feature in the model and check for improvements. Draw insights on the same. Project Task: Week 4 Applying LSTM: Use LSTM for the previous problem (use parameters of LSTM like top-word, embedding-length, Dropout, epochs, number of layers, etc.) Hint: Another variation of LSTM, GRU (Gated Recurrent Units) can be tried as well. Compare the accuracy of neural nets with traditional ML based algorithms. Find the best setting of LSTM (Neural Net) and GRU that can best classify the reviews as positive, negative, and neutral. Hint: Use techniques like Grid Search, Cross-Validation and Random Search Optional Tasks: Week 4 Topic Modelling: Cluster similar reviews. Note: Some reviews may talk about the device as a gift-option. Other reviews may be about product looks and some may highlight about its battery and performance. Try naming the clusters. Perform Topic Modelling Hint: Use scikit-learn provided Latent Dirchlette Allocation (LDA) and Non-Negative Matrix Factorization (NMF).
CapstoneProj-Ecomm-SentimentAnalysis
This repository stores datasets and code for Capstone Project on E-Commerce -Sentiment Analysis
E-commerce-Sentiment-Analysis
Perform sentiment analysis on an e-commerce dataset of 34k Amazon brand product reviews to predict purchase satisfaction using features and review text.
hands-on-rl
Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻🦸🏽
scikit-learn
scikit-learn: machine learning in Python
tensorflow
An Open Source Machine Learning Framework for Everyone
welcome-to-open
Overview of App Academy Open
Desmondonam
Config files for my GitHub profile.
Rossmann_Pharmaceuticals
Pharmaceutical Sales prediction across multiple stores. End-to-end product that delivers this prediction using Streamlit.
Sales-Prediction-for-Pharmaceutical-Distribution-Companies-by-Time-Series-Analysis
Evaluated the sales of a pharmaceutical distribution company using time series sales forecasting models named Seasonal Autoregressive Integrated Moving Average (SARIMA), Prophet, and Support Vector regression (SVR). Assessed and compared the performance of these methods by RMSE and predicted short term sales with the help help of these models.
Rosmann_Pharmacy_prediction
In this project I built an end-to-end product that delivers pharmaceuticals sales prediction to analysts.
Pharmaceutical_Sales_prediction
An end-to-end product that predicts sales across multiple Rossman's pharmaceutical stores.
Rossmann-Pharmaceuticals-Sales-Prediction
An end-to-end product that predicts sales across multiple Rossmans pharmaceutical stores.
start-machine-learning
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2024 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
Pharmaceutical-Sales-_project
Pharmaceutical Sales prediction across multiple stores
Rossmann-Pharmaceuticals-Sales-Prediction
Predict the sales of the pharmaceutical company based on 3 years of data collected on customers and sales across multiple stores.
Pharmaceutical_Sales_prediction
The primary goal of this project is to forecast sales in all Rossmann Pharmaceuticals stores across multiple cities six weeks in advance.
machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
mml-book.github.io
Companion webpage to the book "Mathematics For Machine Learning"
ML-YouTube-Courses
📺 Discover the latest machine learning / AI courses on YouTube.
ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
User_Analytics_in_telecom
User Analytics in the Telecommunication Industry