Kaggle UK Used Car challenge
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Updated
Oct 30, 2021 - Python
Kaggle UK Used Car challenge
Data imputation is used when there are missing values in a dataset. It helps fill in these gaps with estimated values, enabling analysis and modeling. Imputation is crucial for maintaining dataset integrity and ensuring accurate insights from incomplete data.
Modelling the relationship between a player’s first-time eligible arbitration salary and multiple variables.
Streamlit app developed for bank customer deposit prediction, using a fine-tuned XGBClassifier model.
This flask web app is used to detect if a wafer(sensor chip) is default or not based on sensor readings.
[Kaggle Submission] -Using XGBRegressor with shap, grid search and hyperopt to predict house prices
My Capstone for the HarvardX Course "Introduction to Data Science with Python"
Machine learning models for enhanced fraud detection in e-commerce transactions, exploring feature engineering, distance prediction, and clustering analysis.
we perpuse a method to fill nan values using clustering
Filling missed data-points with the most common values among nearest neighbors
This project focuses on predicting customer churn in an e-commerce setting using machine learning techniques.
Built a model to determine the risk associated with extending credit to a borrower. Performed Univariate and Bivariate exploration using various methods such as pair-plot and heatmap to detect outliers and to monitor the behaviour and correlation of the features. Imputed the missing values using KNN Imputer and implemented SMOTE to address the i…
pH Level Forecasting of Well Water Samples in Malawi, Conducted by Leeds Beckett University
the multivariate analysis compares different rows and columns for beat accuracy eg:knn imputer in univariate analysis it only compares with the same columns eg mean or median for numbers
Predicting employee burnout using machine learning algorithms: Random Forest and k-Nearest Neighbors.
The company develops efficiency solutions for heavy industry. The model should predict the amount of pure gold extracted from gold ore. You have the data on extraction and purification. The model will help optimize production and eliminate unprofitable parameters.
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