churn_modelling project

1. INTERNSHIP PROJECT PRESENTATION CHANNABASAVANNA S PAWATE RADHIKA SB SHREYAS M VALI VINAY HS

2. INTRODUCTION Aim:The aim of a churn modeling dataset is to predict customer churn or attrition. Customer churn refers to the phenomenon where customers stop using a company's products or services. Churn modeling involves using historical data about customers, their behaviors, and characteristics to build predictive models that can identify which customers are likely to churn in the future Objective:The simple objective of churn modeling is to predict and reduce customer attrition (churn) by analyzing historical data to identify which customers are likely to leave a product or service, enabling businesses to take proactive measures to retain them. Outcomes: • Churn Predictions: Identification of customers likely to churn, providing a list of at-risk customers. • Improved Customer Retention: Enhanced customer retention, resulting in a larger and more loyal customer base. • Increased Revenue: A boost in revenue attributed to the retention of existing customers. • Data-Driven Insights: Valuable insights into customer behavior and factors influencing churn, aiding future decision-making.

3. CHURN MODELLING • The data, which may involve feature engineering and handling missing values. Machine learning algorithms, such as logistic regression, decision trees, random forests, or more advanced techniques like neural networks, are then employed to build predictive models. The model's performance is evaluated using metrics like accuracy, precision, recall, F1-score, and area under the ROC curve to determine how well it can identify potential churners. • Data preprocessing is a crucial step in machine learning (ML) where raw data is transformed and prepared to be used effectively for training machine learning models. • Proper data preprocessing helps improve the quality of the data, removes noise, handles missing values, and ensures that the data is in a format suitable for the chosen machine learning algorithm • Primarily we import the libraries and load them

4. Common libraries include.Some common libraries are import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score, classification_report, confusion_matrix from sklearn.model_selection import cross_val_score from sklearn.ensemble import RandomForestClassifier # Example model Loading the dataset df = pd.read_csv(df = pd.read_csv("dataset.csv")

5. Data Exploration (EDA):Explore the dataset to gain a better understanding of its structure and contents.Use functions like df.head() to display the first few rows of the DataFrame, df.info() to get information on data types and missing values, and df.describe() to get summary statistics • Visualize the data using plots and graphs to identify patterns and trends. Tools like Matplotlib and Seaborn are helpful for this purpose • Bar graph plotted between NumOfProducts and EstimatedSalary where EstimatedSalary increases when the NumOfProducts increases.

6. Scatter plot:Plotted between EstimatedSalary and Balance • We plot histogram for relationships containing numerical values for eg:AGE:without churn with churn

7. Data Cleaning: Handle missing values: Decide whether to impute or remove rows/columns with missing data. • Handle categorical data: Encode categorical variables using techniques like one-hot encoding or label encoding. categorical_variables = [col for col in df.columns if col in "O" or df[col].nunique() <=11 and col not in "Exited"] categorical_variables • Data Transformation:Feature Scaling: Ensuring that features have similar scales to prevent certain features from dominating others. Common methods include: Normalization,Standardization,Encoding Categorical Variables • Techniques include: 1.One-Hot Encoding 2.Label Encoding 3.Feature Engineering 4. Scaling

8. Data Splitting:Splitting the dataset into training, validation, and test sets to assess model performance. Common splits include 70-30, 80-20, or 90-10 for training-validation and a separate test set. Certainly! Here is a list of six major types of classification algorithms commonly used in machine learning: 1.Logistic Regression:Logistic Regression is a simple and widely used algorithm for binary classification. It models the probability of an instance belonging to one of two classes. Despite its name, it is used for classification, not regression. 2.Decision Trees:Decision Trees are versatile and interpretable algorithms that use a tree-like structure to make decisions. They can be used for both binary and multi-class classification. 3.Random Forest:Random Forest is an ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting. It is effective for various classification tasks. 4.Support Vector Machines (SVM):SVM is a powerful algorithm used for both binary and multi-class classification. It finds a hyperplane that best separates data points into different classes while maximizing the margin

9. 5.Naive Bayes:Naive Bayes algorithms are based on Bayes' theorem and are particularly useful for text classification and spam detection. They assume that features are conditionally independent given the class label 6.K-Nearest Neighbors (KNN):KNN is a simple and instance-based algorithm that classifies data points based on the majority class among their k-nearest neighbors in feature space. It's effective for both binary and multi-class classification.These classification algorithms offer different strengths and are chosen based on factors like the nature of the problem, the size of the dataset, the dimensionality of the features, and the interpretability of the model.

10. Conclusion • In conclusion, churn modeling is a valuable data-driven technique for businesses to predict and reduce customer attrition. • However, successful churn modeling requires attention to data quality, appropriate feature selection, and the use of relevant evaluation metrics. It's an ongoing process that demands regular updates to stay effective in a dynamic business environment. • Churn modeling empowers businesses to proactively address customer churn, enhance customer satisfaction, and maintain a competitive edge in the market. It serves as a vital tool for data-driven decision-making and optimizing customer relationships.

11. THANK YOU