{"id":24968331,"url":"https://github.com/aviraltechie/customer_churn","last_synced_at":"2026-01-07T14:36:43.513Z","repository":{"id":274675160,"uuid":"923153744","full_name":"AviralTechie/Customer_Churn","owner":"AviralTechie","description":"The goal is to analyze customer churn based on factors such as demographics, credit score, financial activity, and account characteristics. 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Here a brief description of the columns:\n\n1. customer_id: Unique identifier (customers).\n2. credit_score: Customers credit scores.\n3. country: Country of residence (e.g., France, Spain).\n4. gender: Gender of the customer (Male/Female).\n5. age: Age of the customer.\n6. tenure: Duration of the customer relationship (years).\n7. balance: Account balance.\n8. products_number: Number of products the customer holds.\n9. credit_card: Whether the customer owns a credit card (1 = Yes, 0 = No).\n10. active_member: Whether the customer is an active member (1 = Yes, 0 = No).\n11. estimated_salary: Estimated annual salary.\n12. churn: Churn status (1 = Churned, 0 = Retained).\n```\n### Dashboard Screenshot\n![Project Image](img1.png)\n![Project Image](img2.png)\n\n### Analysis Approach\n\n`Data Exploration`\n\n1. Understand Trends: Examine distributions of churned vs. retained customers across variables like country, age, gender, and credit score.\n\n2. Correlation: Identify correlations between features like credit score, tenure, balance, and churn rate.\n\n`Dashboard Development in Power BI`\n\nKey Visualizations:\n\n1. Churn breakdown by country (e.g., bar or map chart).\n2. Distribution of churn across credit scores.\n3. Demographic analysis: churn by gender and age groups.\n4. Financial insights: account balance and estimated salary impact on churn.\n5. Interactivity: Allow filtering by region, gender, and other key variables.\n\n`Feature Engineering`\n\n1. Create derived features such as balance-to-salary ratio or tenure categories (e.g., new, mid-term, long-term).\n2. Modeling and Prediction (optional for further analysis):\n\nBuild a predictive model (e.g., logistic regression or decision trees) to identify the probability of churn based on the features.\n\nFile Structure:\n\n`data`: CSV file (e.g., `Prediction.csv`)\n\n`powerbi:` Power BI file (e.g., `churn.pbix`)\n\n\n\n## Data Analysis Using Power BI\n\n```bash\n1. Formula to Create Customer Lost\n= Customer Lost = CALCULATE(COUNT('Customer Data'[Churn]),'Customer Data'[Churn] =\"Churned\")\n```\n```bash\n2. Formula for total number of customer\n= No. of Customers = COUNT('Customer Data'[Customer ID])\n```\n```bash\n3. Formula for churn rate\n=Churn Rate = 'Customer Data'[Customer Lost]/ 'Customer Data'[No. of Customers]\n```\n\n### Clone this repository \n```bash\ngit clone https://github.com/AviralTechie/Customer_Churn.git\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faviraltechie%2Fcustomer_churn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faviraltechie%2Fcustomer_churn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faviraltechie%2Fcustomer_churn/lists"}