The crimes and incarceration in the United States contains data on crimes that are committed and the prisoner count in every 50 states, covering from 2001-2016 where the most interest crime types in the data are violent crime total, murder manslaughter, and aggravated assaults.
The data was analyzed using exploratory data analysis and data visualization to gain further insights about the dataset and understand the relationship between the various independent variables that can be considered in the implementation of the machine learning model.
This analysis was helpful in understanding the crimes that are estimated and the prisoner count in each state based on the various crimes that are committed and to recommend the alertness and strictness in those particular crimes that are increasing the prisoner count.
The Data Analysis Workflow followed for this project is as shown below.
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Data Collection & Preparation
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Exploratory Data Analysis
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Data Cleaning
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Data Visualization
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Pre-Modeling Steps
a. Feature Selection & Extraction
b. Correlation Plot
c. Label Encoding
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Model Building
a. Linear Regression Model
b. Decision Tree Regressor Model
c. Random Forest Regressor Model
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Recommendations & Findings