https://github.com/saylidholam/data_analytics
Data Analytics with Python using Jupyter notebook
https://github.com/saylidholam/data_analytics
csv-files jupyter-notebook
Last synced: 6 months ago
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Data Analytics with Python using Jupyter notebook
- Host: GitHub
- URL: https://github.com/saylidholam/data_analytics
- Owner: SayliDholam
- Created: 2024-02-11T08:37:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-09T08:14:46.000Z (over 1 year ago)
- Last Synced: 2025-03-27T12:12:31.777Z (7 months ago)
- Topics: csv-files, jupyter-notebook
- Language: Jupyter Notebook
- Homepage:
- Size: 161 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data_Analytics
#### Popular Libraries in Python for Data Analytics
##### NumPy:
Provides support for large multi-dimensional arrays and matrices.
Contains a collection of mathematical functions to operate on these arrays.
```import numpy as np```
##### Pandas:
Offers data structures like Series and DataFrame for data manipulation and analysis.
Provides functionalities for reading and writing data, handling missing values, and merging datasets.
```import pandas as pd```
##### Matplotlib:
A plotting library for creating static, interactive, and animated visualizations in Python.
```import matplotlib.pyplot as plt```
##### Seaborn:
Built on top of Matplotlib, it provides a high-level interface for drawing attractive and informative statistical graphics.
``` import seaborn as sns ```
##### Scikit-learn:
A machine learning library that provides simple and efficient tools for data mining and data analysis.
Includes algorithms for classification, regression, clustering, and more.
```from sklearn.model_selection import train_test_split```
```from sklearn.linear_model import LinearRegression```
```from sklearn.metrics import mean_squared_error```
##### SciPy:
Builds on NumPy and provides additional tools for optimization, integration, and other scientific computations.
##### Statsmodels:
Provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests.