A final project in Sharing Vision Data Science Bootcamp
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Updated
Mar 27, 2023 - Jupyter Notebook
A final project in Sharing Vision Data Science Bootcamp
In this project, the objective was to predict house prices in 6 metropolitan cities of India. The dataset provided contained essential features and amenities of houses in these cities. To achieve accurate predictions, a systematic approach was followed, encompassing exploratory data analysis, feature engineering and model building.
Regression model for predicting house prices of residential homes in Ames, Iowa. Dataset contains 79 explanatory variables. Project includes key topics such as dataset cleaning, feature selection/engineering, EDA and applying grid search to find the best model.
House Price Prediction using different regression models like Linear, Ridge, Lasso, Elastic Net, Random Forest, XGBoost, K-Nearest Neighbours, Support Vector Regressor, XGBoost. Also, multi-layer perceptron(MLP) was implemented using TensorFlow
By applying data preprocessing, exploratory data analysis, feature selection, model training, and evaluation techniques, develop a predictive model that can accurately predict the survival status of passengers aboard the Titanic.
Revolutionize sales forecasting for Rossmann stores with our high-accuracy XGBoost model, leveraging data analysis, feature engineering, and machine learning to predict sales up to six weeks in advance.
An ensemble of 3 models - AdaBoost, XgBoost and Random Forests to classify machine failures.
Relation Extraction (spatial and temporal together, person-location-time ) from raw web news text in different languages(English, German, Hindi). Different Natural Language Processing techniques and Machine Learning techniques are exercised. Feature Engineering was the most critical part that I already designed common to all experimenting langu…
Kaggle competition
Predicting House Sales Prices Using Advanced Regression Techniques
Miami Machine Learning Meetup - Feature Learning with Matrix Factorization and Neural Networks
Predict if a review is food relevant of irrelevant
Feature Crawler used for a Fraud Prevention competition
Our repository for Kaggle competition "IEEE Fraud detection"
Apply unsupervised learning techniques to identify segments of a customer base
Feature Engineering on Concrete Strength prediction using Machine Learning Techniques in Python
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