Data_Science_and_Machine_Learning

The work includes the following areas:
Numpy
Pandas
Matplotlib
Seaborn
Exploratory Data Analysis
Feature Engineering
Various ML Models with logic and Scikit-Learn Implementation
Assessment of every concept
Projects of EDA and ML
Deployment and more.....

Note

The Data-Sets used for work in notebooks are standard dataset which can be imported as df = sns.load_dataset('name_of_dataset.csv')

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
Assessments		Assessments
1-NumPy.ipynb		1-NumPy.ipynb
10-Logistic Regression.ipynb		10-Logistic Regression.ipynb
11-Multiclass Classification with Logistic Regression.ipynb		11-Multiclass Classification with Logistic Regression.ipynb
12-KNN.ipynb		12-KNN.ipynb
13-SVM.ipynb		13-SVM.ipynb
14-Decision Tree.ipynb		14-Decision Tree.ipynb
15-Random Forests.ipynb		15-Random Forests.ipynb
16-Boosting Methods.ipynb		16-Boosting Methods.ipynb
17-Naive Bayes and NLP.ipynb		17-Naive Bayes and NLP.ipynb
18-K_Means.ipynb		18-K_Means.ipynb
19-Hierarchical Clustering.ipynb		19-Hierarchical Clustering.ipynb
2-Pandas.ipynb		2-Pandas.ipynb
20-DBSCAN.ipynb		20-DBSCAN.ipynb
21-Principal Component Analysis.ipynb		21-Principal Component Analysis.ipynb
3-Matplotlib.ipynb		3-Matplotlib.ipynb
4-Seaborn.ipynb		4-Seaborn.ipynb
5-Linear Regression.ipynb		5-Linear Regression.ipynb
6-Polynomial Regression.ipynb		6-Polynomial Regression.ipynb
7-Regularization.ipynb		7-Regularization.ipynb
8-Feature Engineering and Data Preparation.ipynb		8-Feature Engineering and Data Preparation.ipynb
9-Cross Validation and Grid Search.ipynb		9-Cross Validation and Grid Search.ipynb
README.md		README.md
svm_margin_plot.py		svm_margin_plot.py

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