Introduction: This R program aims to perform regression analysis on the Breast Cancer Wisconsin dataset obtained from Kaggle. The dataset provides various features of tumor cells, such as mean texture, area, radius, concavity, and more, along with the diagnosis indicating whether the tumor is malignant or benign.

Purpose: The primary objective of this analysis is to build a regression model to predict the diagnosis (malignant or benign) based on the features provided in the dataset. Additionally, exploratory data analysis (EDA) techniques are employed to visualize the data distribution and understand the relationships between variables.

Key Steps:

Data Import and Preprocessing:
    The dataset is imported using the read_excel function from the readxl package.
    Missing values are handled using the mutate function from the dplyr package.

Exploratory Data Analysis (EDA):
    Summary statistics are calculated using the summary function to understand the data distribution.
    Correlation between variables is visualized using a correlation plot generated by the corrplot package.
    Various plots such as boxplots, histograms, and scatter plots are created using ggplot2 to visualize the distribution and relationships between different features.

Regression Analysis:
    A linear regression model is built using the lm function to predict the diagnosis based on selected features such as mean texture, area, radius, concavity, etc.
    Model summary statistics are obtained using the summary function to assess the model's performance and significance of predictors.
    Diagnostic plots such as residual plots are generated to evaluate the assumptions of the regression model.

Conclusion: Through this R program, we have conducted regression analysis on the Breast Cancer Wisconsin dataset to predict the diagnosis of tumors. By exploring the relationships between various features and the diagnosis, we aim to gain insights that can aid in the diagnosis and treatment of breast cancer.