swarna0712 / San-Fransisco-Crime-Classification-using-PySpark

Big Data Project - SSML - Spark Streaming for Machine Learning

Crime-Classification-using-PySpark

Big Data Project - SSML - Spark Streaming for Machine Learning

San Francisco Crime Classification

Link to Dataset

An open-ended project to learn and then train a model to classify crime which is a single vector of size 9 in the dataset to its corresponding label/category. There are 878k records in the train and test dataset.

Design Details

• Streaming To stream the batches, first run stream.py on a terminal and when it shows waiting run streamclient.py on the other terminal. This starts a spark context, session and then converts RDD to a dictionary and then to a dataframe.

• Pre-processing Since the dataset provided is already clean, there are no missing values. We use VectorAssembler for creating a vector of X & Y, and have normalized only the X and Y categories using MinMaxScaler as they are the only numeric categories in the dataset. This shifts and rescales the data such that they are between 0 and 1. We then splitted the Dates column into Day, month , year, hour, minute and second. We have also removed Resolution and descript columns since they are not provided in the test dataset.

We found the correlation between the features and the target column. We implemented both the Pearson and Spearman correlation and obtained the following graphs as outputs. We have dropped Address, seconds and month as they aren't related that much to the category of the crime committed.

Surface Level Implementation

Our goal is to develop (at least 3) a model that is trained incrementally and accurately identifies the type of crime in the test dataset. We used MultiLayerPerceptron, Multinomial NaiveBayes and Stochastic Gradient Descent. We partially fit each of these models on each batch obtained while streaming.

MultiLayerPerceptron

• This model uses a neural network that learns the relationship between linear and non-linear data.

Multinomial NaiveBayes

• The classifier is suitable for classification with discrete features.

Stochastic Gradient Descent

• It implements a plain stochastic gradient descent learning routine which supports different loss functions and penalties for classification. We have used “hinge” as loss function which supports linear SVM.