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caserecommender

Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems

churn-prediction-model-telecommunication

Python - Model Comparison | Logistic Regression, Decision Tree, Random Forest, KNN

ctu_13

An application of machine learning techniques to determining botnet traffic in the Czech Technical University (CTU) 13 dataset.

customer-churn-prediction

Customer Churn Prediction in a Telecommunication Company

data-science-best-resources

Carefully curated resource links for data science in one place

data-science-portfolio

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

data-science-question-answer

A repo for data science related questions and answers

fraud-detection

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

loan-approval-prediction

A machine learning project as a part of college minor project. The prime objective of my project was to use machine learning and data analysis techniques to classify whether the loan of an applicant will be approved by the bank or not.

machine-learning-with-python

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

matplotlib

matplotlib: plotting with Python

ml-fraud-detection

Credit card fraud detection through logistic regression, k-means, and deep learning.

movie-recommendation-system-with-python

Basic recommendation system by suggesting movies that are most similar to a particular movie.

normalization_vs_standardization

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

parkinson-detection-with-xgboost

In this Python machine learning project, using the Python libraries scikit-learn, numpy, pandas, and xgboost, we will build a model using an XGBClassifier. We’ll load the data, get the features and labels, scale the features, then split the dataset, build an XGBClassifier, and then calculate the accuracy of our model.

probability_cheatsheet

A comprehensive 10-page probability cheatsheet that covers a semester's worth of introduction to probability.

python_and_excel

tutorials and notebooks on using python where excel fails or in combination with excel

recommendation_system_for_supermarket

This is a Basic Project with limited Dataset created by own.

speech_recognition_and_emotion_detection_in_english_and_german

Python project for Speech-to-Text and Sentiment Analysis. Supports English and German language!

surprise

A Python scikit for building and analyzing recommender systems

telecommunicationchurnanalysisprediction

As the regular day-to-day activities are completely subjected to the utilization of telecom products and its services, the global market for telecommunication is escalated to grow at a phenomenal rate over the coming years. It is more important for the telecom industries to save their customers. The officials of the telecom industry must find their ways to improve the customer strength while maintaining the current customer rate and also retaining back old customers. The process where one customer leaves one company and joins another is called as churn. Churn is a very important area in which the telecom domain can make or lose their customers and hence the business/industry spends a lot of time doing predictions, which in turn helps to make the necessary business conclusions. Churn can be avoided by studying the past history of customers. The powerful weapon in today’s telecom industry is keeping the existence customers and acquiring new customers. Since the churn customers are increasing which brings the domains experts in action to make necessary churn analysis of customers. Churn prediction can be implemented through various supervised machine learning models. The company introduces new techniques and applications to increase the services to retain the customers. Various telecom companies are coming with advanced tactics in order to predict the churned customer in early stage. Traditionally, various types of machine learning approaches like Decision tree, Random Forest, and Bagging etc., were applied to predict churned customer. According the literature survey, the churn predictions for telecom industries also uses deep learning techniques for better accuracy and low processing time.