my-machine-learning-projects-ct/kaggle-crime-master

[Data Science] An end to end project to explore & visualize crime data and predict category of crime in San Francisco.

Kaggle San Francisco Crime Classification

An end to end project to explore, visualize, and analyze San Francisco crime data and predict category of crime given temporal and spatial features.

Installation

All Python packages required for this project are located in the requirements.txt and can be installed via the command below.

$ pip install -r requirements.txt

Files

Data Exploration & Visualization

• kaggle-sf-crime-exploration.ipynb - jupyter notebook with initial data explorations & visualizations of SF crime

Data Mining & Machine Learning

• kaggle-sf-crime-prediction.ipynb - jupyter notebook with end to end data science workflow, such as data preprocessing, feature engineering, building baseline models, model selection, hyperparameter tuning, and Kaggle submission.

Folders

Data Visualizations

• visualizations - folder containing visualizations of the data (barplots, scatterplots, heatmaps, maps, etc.)

Hyperparameter Tuning Results

• cv_results - folder containing the hyperparameter tuning results (CV scores (mean & standard deviation) and hyperparameters) at each iteration of Bayesian Optimization.

Dataset

Dataset contains incidents derived from SFPD Crime Incident Reporting system. The data ranges from 1/1/2003 to 5/13/2015 (~12 years worth of data). The training set and test set rotate every week, meaning week 1,3,5,7,... belong to test set, week 2,4,6,8,... belong to training set.

Data Fields

• Dates - timestamp of the crime incident
• Category - category of the crime incident (only in train.csv). This is the target variable you are going to predict.
• Descript - detailed description of the crime incident (only in train.csv)
• DayOfWeek - the day of the week
• PdDistrict - name of the Police Department District
• Resolution - how the crime incident was resolved (only in train.csv)
• Address - the approximate street address of the crime incident
• X - Longitude
• Y - Latitude

The source of the dataset can be found in the following links:

• Kaggle Source
• Original Source

Visualizations

Some visualizations of the spatial and temporal features along with category of crime.

Heatmap of SF police district given category of crime

Lineplot of year given category of Crime

Kaggle Submission

Achieved a multi-class log loss of of 2.25674, which would ideally rank at #136 (out of 2,335 teams) or at the top 6% or 94th percentile on the public leaderboard. More details can be found in this notebook: kaggle-sf-crime-prediction.ipynb

License

See the LICENSE file for license rights and limitations (MIT).