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2023-01-data-science-exercise

2023-01 ICRAR Data Science applicants exercise Completed

automatic-valuation-model-for-ga_housing

churn-modeling

Churn Prediction, Customer Clustering, PCA, and EDA on customer data

clustering_for_single_family_residence_investors_in-ga

### About this Project This is a partnership research between NYC Data Science Academy and HaystackAI. We used the latest Machine Learning sk-learn models for descriptive data analysis in Python. Objective of The Project: To provide insight on how to use clustering to identify outliers in the housing market, characterise and identify different kinds of anomalies, and identify opportunities for investment in each neighbourhood. To provide data backed insight for Single family Rensidence( SFR) PropTech investor who wish to invest in a place with long term growth by highlighting different areas with increased gentrification. To Provide diverse metrics to identify unappreciated opportunities and enable real estate professionals to be ahead of the market
. The Motivation of The Project: The traditional use of ZipCodes for demarcation of local areas have limited use in the business context as there can be different zip codes that represent the same market and vice versa. We try to use Machine learning Clustering techniques to determine the natural group of clusters for Single Family Residence. The Data: The data used for this project was provided by HaystackAI and protected under extant copy right laws. Other alternative sources of data that are publicly available were from Broker Listing Data,Crime_Diary, Census, Local News, Amenities, Finance and were downloaded from the assessors website as text files and contained both categorical and continuous data. We used unsupervised learning method for this project, created clusters of properties with geocoded locations to identify properties and their locations with similarities over a large number of features. Principal Component Analysis PCA, was used for dimensionality reduction, and unsupervised clustering was performed using k-means, hierarchical agglomerative clustering (HCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using scikit-learn libraries. We identified seven natural clusters with unique descriptions and diverse investment potentials. We tested different clustering techniques including Kmeans with different initialisations, MiniBatchKmeans, Agglomerative Hierarchical Clustering and DBSCAN. We used the Elbow Method and Silhouette scores metrics for determining the optimum number of clusters. Cluster 1 , for example, has these characteristics, lower cost, very high crime rate, very high rate of growth, fewer amenities. It is likely attractive for investors that target lower income buyers and who would like to maximise ROI. Similarly, Cluster 3 has these characteristics, lower cost, stable crime rate, very high rate of growth-good amenities. It is ideal for medium term investors.

coderbyte-challenges-solutions

CoderByte-Challenges-Solutions

com.buchi

Java repository

credit-risk-classification-model-for-banks-and-digital-marketing

Accurate segmentation of clients is crucial in the success of any business enterprise. It serves the dual purpose of helping the business deal with the client based on their types and also in digital marketing in creating lookalikes. This study is about the binary classification of loan seeking bank customers based on whether they are likely to defualt or not. When a customer seeks for a loan, banks and other credit providers should use statistical models to determine whether or not to grant the loan based on the likelihood of the loan being repaid or not. The factors involved in determining this likelihood are complex, and extensive statistical analysis and modeling are required to predict the outcome for each individual case. This model predicts loan repayment or default based on the data provided.

data-engineering-zoomcamp

Free Data Engineering course!

machinelearning-deeplearning-code-for-my-youtube-channel

The full collection of all codes for my Youtube Channel segregated as per topic.

maejok

Config files for my GitHub profile.

mastering-apache-spark

This is repository of my YouTube Course on End to End Apache Spark in AIEngineering YouTube Channel

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

ml_price_prediction_model_for_ames_housing

This project is on descriptive and predictive analysis of Ames Housing Data. We wish to: Determine the factors that affect the prices of the local housing market? Train a model to accurately predict the prices and improve price prediction accuracy using ML techniques and also test that our predictions are realistic using GridSearchCV, XGBoost etc.

nanogpt

The simplest, fastest repository for training/finetuning medium-sized GPTs.

python-crypo-project

Crypto Currency performance appraisal in python for NYCDSA

pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

service_rating-prediction-for-restaurants-in-xyz_city

test

NYC DSA test

tutorials

Git Repo for Articles on Ergo Sum blog and the youtube channel https://www.youtube.com/channel/UCiie9CN--dazA7iT2sry5FA