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
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.
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.
An ensemble of 3 models - AdaBoost, XgBoost and Random Forests to classify machine failures.
Notebook image and notebook for feature reduction talk
Project for "Int20h" Hackathon (Kyiv, Ukraine 2018). First place in "Data Science" branch.
Predicting the Gender of the riders of New York Citi Bikes (2015-2017)
Use of kmeans segmentation algorithm to classify dermis, epidermis and tumor infiltration.
A Comprehensive Guide to Titanic Machine Learning from Disaster
SQL and NoSQL Databases
Collect sensor data from Android cell phone.Using accelerometers and gravimeters to calculate horizontal and vertical accelerations. Use decision tree as classifier
I worked on Breast Cancer Wisconsin (Diagnostic) Data Set and I made predict with Sci-Kit Learn library.
Machine Learning modelling for a classification problem.
Predicting the time remaning for the next Earthquake. Kaggle Competition
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