The Data science project which is given here is an analysis of employee performance

Data were analyzed by describing the features present in the data. the features play the bigger part in the analysis. The features tell the relation between the dependent and independent variables. Pandas also help to describe the datasets answering following questions early in our project. The data present in the dataset are divided into numerical and categorical data.

Library Used: Matplotlib & Seaborn Plots Used: Histplot, Lineplot, CountPlot, Barplot

Basic Check & Statistical Measures* Their is no constant column is present in Numerical as well as categoriacl data.

1. Check Missing Value: Their is no missing value in data

2. Categorical Data Conversion: Handel categorical data with the help of frequency and mannual encoding, because feature is contain lot's of labels

3. Outlier Handling Some features are contain outliers so we are impute this outlier with the help of IQR because in all features data is not normally distributed

4. Feature Transformation: In YearsSinceLastPromotion some skewed & kurtosis is present, so we are use Square Root Transformation techinque

5. Scaling The Data: scaling the data with the help of Standard scalar

1. Drop unique and constant feature: Dropping employee number because this is a constant column as well as drop Years Since Last Promotion because we create a new feaure using square root transformation

2. Checking Correlation: Checking correlation with the help of heat map, and get the their is no highly correlated feature is present.

3. Check Duplicates: In this data Their is no dupicates is present.

4. PCA: Use pca to reduce the dimension of data, Data is contain total 27 feature after dropping unique and constant column,from PCA it shows the 25 feature has less varaince loss, so we are going to select 25 feature.

5. Saving Pre-Process Data: save the all preprocess data in new file and add target feature to it.

1. Define Dependant and Independant Features:

2. Balancing the data: The data is imbalance, so we need to balance the data with the help of SMOTE

3.Splitting Training And Testing Data: 80% data use for training & 20% data used for testing

• AIM: Create a sweet spot model (Low bias, Low variance)

HERE WE WILL BE EXPERIMENTING WITH THREE ALGORITHM

1. Support Vector Machine
2. Random Forest
3. Artificial Neural Network [MLP Classifier]

Save model with the helpof pickle file

Jupyter

• Pandas
• Numpy
• Matplotlib
• Seaborn
• pylab
• Scipy
• Sklearn
• Pickle