A New, Interactive Approach to Learning Python

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Karma of Humans is AI

An anytime implementation of scikit-learn GridSearchCV

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

Creating Predictions for Numerai with Keras and scikit-learn

In this project, I employ several supervised algorithms to accurately predict an individual income using data collected from the 1994 U.S. Census. We implement various testing procecures to choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data.

Learn and Explore

Classify pictures by architectural style and recognize objects with CNNs and YOLO

Digit recognition using SVM

PREDICT TUMOR IS MALIGNANT OR BENIGN

Spam Detection – Cluster SMS messages to “Spam” and “Ham” (Kaggle Challenge)

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.

It is an attempt to bring along various techniques in machine learning and deep learning

Sentiment Analysis of Tweets related to Vaccine.

Prediction of bike rental count hourly or daily based on the environmental and seasonal settings using data set from two-years historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA

This project is to classify breast cancer using SVM-Classifier and further improving the model using GridSearch

Walmart Sales Forecast Solution

Hyperparameter tuning using Sklearn Pipelines

A case study on predicting customer churn using machine learning.

Bangla Music Mood Detect Pattern Lab Project

Hyperparameter Tuning done on Random Forest Classifier using Hyperopt over Pima Diabetes Dataset!!

The main of this solution is to identify the churn customers and to find important features for identifying churn customer.

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.

Become a proficient, productive and powerful programmer with Python

Contains all my data science projects.

Kaggle Competition (Encoding categorical variables)

The problem deals with classifying sentences into 5 categories