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Different prediction algorithms that calculate the selling price of properties in Buenos Aires City. Languages and Technologies: Python, Pandas, Machine Learning Algorithms, Jupyter Notebook, Google Maps API.
Got 55th rank in Machine Learning Challenge out of 3347 competiting participants held on Hackerearth, with an accuracy of 95.841% using Random Forests and XGBoost.
This project studies and visualizes the original data and creating new attributes in the dataset which will cater to the exploratory data analysis. This is a small attempt into viewing this problem as a classification problem using a simple XGBoost model that provides a basic prediction (still working on the final part).
In this project, we will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.
The classical data science intro with Kaggle's titanic data set is used to illustrate a simple but entire data science life-cycle including model optimization, train-test-split, eda and cleaning.