https://github.com/rakibhhridoy/uscrimeanalysis-appliedstatistics
Detecting the key reason for crime(manslaughter) happened in 30 years. There's a lot of aspect present in the data published by US government. The data and the finding can be used in any country prospect,if not all but handy few.
https://github.com/rakibhhridoy/uscrimeanalysis-appliedstatistics
ab-testing applied-statistics crime crime-analysis crime-prediction crime-statistics eda exploratory-data-analysis exploratory-data-visualizations hypothesis-testing machine-learning python statistical-analysis statistical-learning statsmodels visualization
Last synced: 5 months ago
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Detecting the key reason for crime(manslaughter) happened in 30 years. There's a lot of aspect present in the data published by US government. The data and the finding can be used in any country prospect,if not all but handy few.
- Host: GitHub
- URL: https://github.com/rakibhhridoy/uscrimeanalysis-appliedstatistics
- Owner: rakibhhridoy
- Created: 2020-07-13T15:44:45.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-08-04T19:39:24.000Z (about 5 years ago)
- Last Synced: 2025-02-17T02:41:59.487Z (8 months ago)
- Topics: ab-testing, applied-statistics, crime, crime-analysis, crime-prediction, crime-statistics, eda, exploratory-data-analysis, exploratory-data-visualizations, hypothesis-testing, machine-learning, python, statistical-analysis, statistical-learning, statsmodels, visualization
- Language: Jupyter Notebook
- Homepage: https://rakibhhridoy.github.io
- Size: 6.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### US Crime Analysis Applied Statistics ```python```
##### This Repo consist of the statistical analysis and applied frequentist and bayesian statistics to transform, manipulate and finding key aspect of crime. It is Based on only two categorical incident.. ```1. murdered 2. Manslaughter by negligence```
##### In this project, we use mostly
- ```numpy``` for numerical and mathematical operations
- ```pandas``` DataFrame Manipulation and Handling
- ```matplotlib``` Visualizing our Data
- ```seaborn``` Visualizing data in more compact form
- ```statsmodel``` Different Statistical Computing
##### If you don't have those libraries,you can install it by pip
```bash
pip install numpy
pip install pandas
pip install matplotlib
pip install seaborn
pip install statsmodel
```
##### To use those simply import them in your notebook
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodel.api as sm
import seaborn as sns
```
##### the project is on going..
you can follow me/hire me in [linkedin](https://linkedin.com/in/rakibhhridoy)