Software Engineer - L2 at Gemini Solutions Pvt Ltd

Drashti Maheshwari  • Peerlist

Our motive is to predict the features on which the customer attrition depends and predict the whether the customer will continue or switch the service.
Dimensions and explanation of the DataSet
customerID: Customer ID
gender: gender (female, male)
SeniorCitizen: Whether the customer is a senior citizen or not (1, 0)
Partner: the customer has a partner or not (Yes, No)
Dependents: Whether the customer has dependents or not (Yes, No)
tenure: Number of months the customer has stayed with the company
PhoneService: Whether the customer has a phone service or not (Yes, No)
MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService: Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup: Whether the customer has online backup or not (Yes, No, No internet service)
DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport: Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract: The contract term of the customer (Month-to-month, One year, Two year)
PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)
PaymentMethod: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges: The amount charged to the customer monthly
TotalCharges: The total amount charged to the customer
Churn: Whether the customer churned or not (Yes or No)
Model_Applied  Accuracy
1. Logistic_Regression = 0.785140
2. Model_DecisionTree = 0.712257
3. RandomForest = 0.766209
4. KNN = 0.755797

Customer Churn Analysis

This project is the data analysis and data exploration on IPL Dataset to know the insights of the IPL Data.
->Libraries used in this project:
    *Numpy
    *Pandas
    *Matplotlib
    *Seaborn
->Attributes of the Dataset are:
*id - the unique ID of any particular match
*season - year or session in which the match was held
*city - city where match was conducted
*date - date of the match
*team1 - team played the match
*team2 - another team which played the match
*toss_winner - team who won the toss in any particular match
*toss_decision - decision took by team after winning the toss i.e. bat or field
*result - result of the match i.e. tie or normal
*dl_applied - DLS (Duckworth - Lewis - Stern) Method applied or not
*winner - winning team of the match
*win_by_run - runs by which team won the match
*win_by_wickets - wickets by which team won the match
*player_of_match - Man or Player of the match
*venue - Stadium where match was conducted
*umpire1 - 1st umpire on the field
*umpire2 - 2nd umpire on the field
*umpire3 - 3rd umpire of the match ##### The Duckworth–Lewis–Stern method (DLS) is a mathematical formulation designed to calculate the target score for the team batting second in a limited overs cricket match interrupted by weather or other circumstances.