Toronto MCI Analysis & Prediction Model

This project is a Python script that analyzes the MCI (Multi-Criteria Indicator) scores for different neighborhoods in Toronto. The script first imports the necessary libraries and then loads the MCI dataset. The dataset contains information about the MCI scores for different neighborhoods in Toronto, as well as other information about the neighborhoods, such as the population density, the average income, and the crime rate. The script then uses the scikit-learn library to build a regression model. The model is used to predict the MCI scores for different neighborhoods. The model is trained on the MCI dataset, and it is then evaluated on a held-out test set. The model is able to achieve a high accuracy on the test set, which shows that it is able to predict the MCI scores for different neighborhoods with a high degree of accuracy. The script also includes a section on how to visualize the results of the analysis. The results of the analysis are visualized using a scatter plot, which shows the relationship between the MCI scores and the other variables in the dataset. The project toronto_mci_analysis_and_prediction_model.py is a great way to learn how to use the scikit-learn library for regression modeling. The script is well-organized and easy to follow, and it includes clear explanations of the code. Here are some additional resources that you may find helpful:

• scikit-learn website: https://scikit-learn.org/stable/

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/

• Regression with scikit-learn: A tutorial: https://scikit-learn.org/stable/modules/linear_model.html

I hope this helps!

https://github.com/AqsaAshfaq07/Machine-Learning-with-Python-Libs/blob/main/toronto_mci_analysis_and_prediction_model.py