EMPLOYMENT CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

We are using Classification techniques like CART, Logistic Regression, Random Forest,Decision tree, Neural network , Perceptron ,to classify the price range of the mobile phones.

Modules involved:

1. Loading Data , pre processing our data
2. Visualizing our Data , finding target label
3. Splitting the data into training samples and testing samples
4. Using classification techniques and finding the accuracy of the model
5. Analyzing different classification metrics like Precision , Recall , Accuracy etc.
6. Concluding the best model.

About The Data

I took this data set from kaggle , it consists of 2 csv files , I used train.csv file, the columns are

partic, hincome, children, region

1.Loading Data , pre processing our data

Lets load data downloaded from Kaggle.

2.Visualizing our Data , finding correlation among features and target label

3.Splitting the data into training samples and testing samples

we just used sklearn library to split into train,test and we divided them into 70–30 ratio.

4.Using classification techniques and finding the accuracy of the model

5.Analyzing different classification metrics like MSE, RMSE , Precision , Recall , Accuracy etc.

we already analysed them in the previous steps

6.Concluding the best model.

Among all the classifiers we choose decision tree with entropy has highest accuracy.

Thank You

B.Vasanth