rahulkmsharma / 267646_data-science-london-scikit-learn

267646_Data-Science-London-Scikit-learn

This Project is mainly focus on learning of Different classification algorithms

the whole project is divided into 2 categories

1 . Data Pre-Processing 2 . Model Building

Data Preprocessing

This project doesn't required too much data pre-processing technique because it mainly focuses on applying different machine learning algorithms and selecting which algorithms is best suited for my dataset Data Preprocessing is maily consist of two parts 1 . Splitting Data into test and train 2 . Data Normalisation and Standardization

Model Building

I have applied different classification algorithms which are stated as below with observed accuracy 1 . Logistic Regression

Algorithms	Type	Accuracy
Logistic Regression	Simple	83.0%
Logistic Regression	Data Normalized	78.5%
Logistic Regression	Data Standardized	80.5%

2 . Decision Tree

Algorithms	Type	Accuracy
Decision Tree	Simple	73.5%
Decision Tree	Data Normalized	43.0%
Decision Tree	Data Standardized	65.5%

3 . SVM(Support Vector Machine)

Algorithms	Type	Accuracy
SVM	Simple	86.5%
SVM	Data Normalized	62.5%
SVM	Data Standardized	68.5%

4 . KNN(K Nearest Neighbour)

Algorithms	Type	Accuracy
KNN	Simple	88.0%
KNN	Data Normalized	86.5%
KNN	Data Standardized	88.0%

5 . Random Forest

Algorithms	Type	Accuracy
Random forest	Simple	85.5%
Randm Forest	Data Normalized	73.0%
Random Forest	Data Standardized	84.0%

Conclusion

From above results i conclude that KNN is best suited algorithm for this problem therefore finally i am applying KNN algorithm to get output