Bank Customer Behaviour Prediction
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Updated
Mar 20, 2024 - Jupyter Notebook
Bank Customer Behaviour Prediction
Exploratory Data Analysis for HR dataset
This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
Artificial Neural Network using Keras in python to identify customers who are likely to churn.
Predictive model that tells important factors(or features) affecting the demand for shared bikes
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
Time Series Model
Cloud image generation with Python and OpneCV
In this project we will apply Recurrent Neural Network (LSTM) which is best suited for time-series and sequential problem, we will be creating a LSTM model, train it on data and make predictions to check its performance.
Stock price prediction is the process of forecasting future stock prices based on historical data and market indicators.
Feature transformation is a technique in machine learning that changes the way features are represented in order to improve the performance of machine learning algorithms. This can be done by transforming the features to a different scale, removing outliers, or creating new features from existing
Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
Created machine learning models capable of classifying candidate exoplanets from a raw dataset.
Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.
Linear Regression+Decision Tree+Random Forest
Telecommunication Company Churn Project
This project is based on a classification algorithm i.e. Naive Bayes which is run on a mobile dataset consisting of 2000 rows and 15 columns. It is a multi-class problem where mobile phones are classified in accordance with their price range. There are four classes of price ranging from 0 to 3, 0 indicating cheaper mobiles phones and 3 represent…
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