From 5b732b9d0480f11f781bc2190567c4d65b397b55 Mon Sep 17 00:00:00 2001 From: Gokulpriyaguru Date: Sun, 5 Oct 2025 00:00:57 +0530 Subject: [PATCH] =?UTF-8?q?Create=20=F0=9F=91=89=20EDA=5FCarInsuranceClaim?= =?UTF-8?q?.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Understand the Project Requirements GUVI’s Car Insurance Claim Prediction project usually expects: Data Cleaning & EDA (Exploratory Data Analysis) — using Python (Pandas, Matplotlib, Seaborn). SQL Analysis — running queries to extract insights. Machine Learning Model — to predict whether a customer will file an insurance claim. Power BI Dashboard — to visually represent insights and model outcomes. Source Code Submission — your .ipynb, .sql, .pbix, and report files. --- ...0\237\221\211 EDA_CarInsuranceClaim.ipynb" | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 "\360\237\221\211 EDA_CarInsuranceClaim.ipynb" diff --git "a/\360\237\221\211 EDA_CarInsuranceClaim.ipynb" "b/\360\237\221\211 EDA_CarInsuranceClaim.ipynb" new file mode 100644 index 000000000..7d87ab7dc --- /dev/null +++ "b/\360\237\221\211 EDA_CarInsuranceClaim.ipynb" @@ -0,0 +1,20 @@ +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt + +# Load dataset +df = pd.read_csv("car_insurance.csv") + +# Basic info +print(df.info()) +print(df.describe()) +print(df.isnull().sum()) + +# Data cleaning +df.drop_duplicates(inplace=True) +df.fillna(df.median(), inplace=True) + +# Visualization +sns.countplot(x='Vehicle_Age', hue='Claim', data=df) +plt.title("Vehicle Age vs Claim Probability") +plt.show()