Toronto_hoods_housing

Project Explores Toronto Neighborhoods and Housing using a variety of data science and machine learning techniques.

File Contents

• C1_Toronto_Neighborhoods

  • Notebooks about Toronto neighborhood analysis

• C2_Toronto_Housing

  • Notebooks about Toronto housing analysis

• dimension_excercise

  • Notebooks about dimension reduction practise

• old_files

  • old version of Notebooks for storage

Skills Covered (ongoing)

• Clustering

  • K means

  • Hierarchical

• Dimensionality reduction

  • Regression Matrix

  • PCA

  • TSNE

  • UMAP

• Similarity

  • In terms of Manhattan distance

  • In terms of Euclidean distance

• Classification

  • Binary classification

    • Decision trees

  • Multi-class classification

    • Decision trees

• Visualization

  •   Type
    

    • Bar graph

    • Scatter line

    • Line plot

    • Pie chart

    • Histogram

    • Box plot

  • APIs

    • Matplotlib

    • Seaborn

    • Plot.ly

• Regression

  • Simple linear regression

  • Multiple Linear regression

  • LASSO

  • Logistic

  • Support vector Machines

  • Random Forest

• Natural Language Processing

  • Sentiment analysis - textblob

  • Text mining

  • Part of speech analysis - NLTK

  • Inclusion of phrase comparison with target variable

• Map

  • API's

    • Geoplot

    • Folium

    • Plot.ly

  • Skills:

    • Add pins

    • Add lines

    • Add areas

    • Add geometry from file - geojson

• Time Series

  • AR

  • MA

  • ARIMA

  • ARIMAX

  • SARIMA

  • SARIMAX

  • Vector AutoRegression

• Neural Network 

  • Tensorflow

    • Binary classification

    • Multi classification

  • Regression

    • Tensorflow probability