diff --git a/Machine Learning and Data Science/Basic/Insurance Prediction Using R/insurance.csv b/Machine Learning and Data Science/Basic/Insurance Prediction Using R/insurance.csv
new file mode 100644
index 000000000..24f62058c
--- /dev/null
+++ b/Machine Learning and Data Science/Basic/Insurance Prediction Using R/insurance.csv	
@@ -0,0 +1,1339 @@
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diff --git a/Machine Learning and Data Science/Basic/Insurance Prediction Using R/readme.md b/Machine Learning and Data Science/Basic/Insurance Prediction Using R/readme.md
new file mode 100644
index 000000000..58848645e
--- /dev/null
+++ b/Machine Learning and Data Science/Basic/Insurance Prediction Using R/readme.md	
@@ -0,0 +1,57 @@
+# Health Insurance Charges Prediction
+
+We are analyzing a health insurance dataset using linear regression to predict insurance charges based on several factors like age, sex, BMI, number of children, smoking status, and region.
+
+## Steps
+
+### 1. **Loading and Inspecting Data**
+The first step is to load the dataset using `read.csv()` and inspect the first few rows with `head(df)`. This helps in understanding the structure of the dataset and the variables it contains.
+
+### 2. **Identifying Numeric Columns and Plotting**
+We use `lapply()` combined with `is.numeric` to identify numeric columns in the dataset. The `plot()` function then generates plots for all numeric columns to visualize the distribution and relationships between these variables.
+
+### 3. **Correlation Matrix**
+Next, a correlation matrix is calculated using `cor()` to examine how the numeric variables relate to each other. The output is rounded to two decimal places for better readability.
+
+### 4. **Converting Categorical Variables to Factors**
+The variables `smoker`, `sex`, and `region` are categorical, so they are converted into factors using the `as.factor()` function. In R, factors are used to represent categorical data, and this ensures the variables are treated correctly in the regression model.
+
+### 5. **Boxplot Visualizations**
+Boxplots are created to visually analyze how different categories (smoker, sex, region) influence the `charges` variable. These plots allow us to compare the distribution of insurance charges between smokers and non-smokers, males and females, and different regions.
+
+### 6. **Building the Linear Regression Model**
+A linear regression model is built using the `lm()` function with `charges` as the dependent variable and all other variables as independent predictors. The `summary(model1)` function provides details of the model, such as the coefficients, p-values, and R-squared value, which help us assess the model's performance and the significance of the predictors.
+
+### 7. **Making Predictions for New Data**
+To make predictions for new customers, the `sex`, `smoker`, and `region` columns are converted back into factors. A new data frame is created for multiple new customers with relevant features, and `predict()` is used to estimate the insurance charges for these individuals.
+
+### 8. **Displaying Predictions**
+The predicted charges for the new customers are printed to the console. These predictions help in estimating how much an individual might pay for health insurance based on their characteristics.
+
+## Key Insights
+
+- **Correlation**: The correlation matrix reveals relationships between variables like age, BMI, and insurance charges, which are essential for predicting charges.
+- **Boxplots**: The boxplots indicate that smokers generally incur higher insurance charges than non-smokers, and there are differences in charges across genders and regions.
+- **Linear Regression Model**: The linear regression model captures the relationship between various factors and insurance charges. Significant predictors include age, BMI, smoking status, and region.
+
+## Conclusion
+
+This analysis demonstrates how a linear regression model can be used to predict health insurance charges based on demographic and health-related variables. The model highlights key factors influencing charges, such as smoking and BMI, and can be used to predict charges for new customers.
+
+![Screenshot 2024-11-06 at 11 17 32 PM](https://github.com/user-attachments/assets/ddc1ae6b-da07-4184-a56b-3a18f3a0f5d7)
+
+![Screenshot 2024-11-06 at 11 17 48 PM](https://github.com/user-attachments/assets/a839c1db-d8d0-49de-a37c-0b28a97d611d)
+
+![Screenshot 2024-11-06 at 11 18 07 PM](https://github.com/user-attachments/assets/7c4b4cfa-768b-4b62-b0cf-2dab20eb787d)
+
+![Screenshot 2024-11-06 at 11 18 15 PM](https://github.com/user-attachments/assets/ff95ee78-95c8-4e6c-acd1-29199f33c1ec)
+
+
+## Future Steps
+
+- Explore more advanced machine learning models like decision trees, random forests, or gradient boosting to improve prediction accuracy.
+- Experiment with feature engineering techniques such as interaction terms or polynomial features.
+- Use regularization techniques like Lasso or Ridge regression to reduce overfitting and improve generalization.
+
+This process provides a solid foundation for predicting insurance charges based on available data, which can be expanded with additional variables or more sophisticated models.
+
diff --git a/Machine Learning and Data Science/Basic/Insurance Prediction Using R/regression_health_insurance.Rmd b/Machine Learning and Data Science/Basic/Insurance Prediction Using R/regression_health_insurance.Rmd
new file mode 100644
index 000000000..a6e636b17
--- /dev/null
+++ b/Machine Learning and Data Science/Basic/Insurance Prediction Using R/regression_health_insurance.Rmd	
@@ -0,0 +1,61 @@
+df = read.csv('/Users/sanatkumargupta/Desktop/data_science_mini_projects/regressin_health_insurance/insurance.csv', header=TRUE)
+
+head(df)
+#identify numeric colums
+num_cols <- unlist(lapply(df, is.numeric))
+#unlist returns logical vector t/f  --- it convert list by lapply to vector
+plot(df[,num_cols])
+#plot graph for all x such that num col is true
+
+round(cor(df[,num_cols]),2)
+#cor() function computes the correlation matrix for the selected numeric columns
+
+smoker = as.factor(df$smoker)
+# as.factor() function converts the smoker column into a factor. In R, factors are used to handle categorical variables.
+head(df)
+# factor is be be treated as category and not as numeric or text
+sex = as.factor(df$sex)
+region = as.factor(df$region)
+
+# tilde ~ separates the dependent and independent variables in formulas in R.
+#smoker is the independent (categorical) variable, which has been previously defined as a factor (e.g., "Yes" or "No" for smoker status).
+#main = 'smoker':
+#This specifies the title of the plot. The text 'smoker' will appear as the title above the boxplot.
+
+boxplot(df$charges ~ smoker, main = 'smoker')
+boxplot(df$charges ~ sex, main = 'sex')
+boxplot(df$charges ~ region, main = 'region')
+
+model1 = lm(charges ~. , data = df)
+summary(model1)
+
+# lm -> linear regression model
+# charges ~ .: The formula here indicates that you're modeling the charges variable (dependent variable) as a function of all other variables in the data frame df (independent variables).
+#The dot (.) means "all other columns in df except for charges". So, all predictor variables in the dataset will be included in the model.
+
+# make prediction
+# Convert columns to factors
+df$sex <- as.factor(df$sex)
+df$smoker <- as.factor(df$smoker)
+df$region <- as.factor(df$region)
+# Check levels in the original data
+levels(df$sex)        # Ensure "male" and "female" are present
+levels(df$smoker)     # Ensure "yes" and "no" are present
+levels(df$region)     # Check all levels present in df
+# Create a data frame for multiple new customers
+new_data_multiple <- data.frame(
+  age = c(30, 45),
+  sex = factor(c("male", "female"), levels = levels(df$sex)),
+  bmi = c(22.5, 30.0),
+  children = c(0, 2),
+  smoker = factor(c("no", "yes"), levels = levels(df$smoker)),
+  region = factor(c("southwest", "southeast"), levels = levels(df$region))
+)
+
+# Make predictions for multiple new customers
+predicted_charges_multiple <- predict(model1, newdata = new_data_multiple)
+
+# Display the predicted charges for multiple customers
+print(predicted_charges_multiple)
+
+
diff --git a/Machine Learning and Data Science/README.md b/Machine Learning and Data Science/README.md
index 3af315cc6..da0f77480 100644
--- a/Machine Learning and Data Science/README.md	
+++ b/Machine Learning and Data Science/README.md	
@@ -25,7 +25,7 @@
 | 19. | [ Sea Level Predictor ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Sea%20Level%20Predictor) | 20. | [ Single Neural Network ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Single%20Neural%20Network) | 21. | [ Super Resolution Using Deep Learning ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Super_Resolution_Using_Deep_Learning) |
 | 22. | [ Titanic Survival Prediction ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Titanic_Survival_Prediction) | 23. | [ Uber Analysis ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Uber%20Analysis) | 24. | [ Walmart Analysis ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Walmart%20Analysis)
 | 25. | [ Water Quality Data EDA ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Water%20Quality%20Data%20EDA) | 26. | [ Wine Quality Prediction ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Wine%20Quality%20Prediction) | 27. | [ Word Cloud ](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Word-Cloud)  
-| 28. | [Text Summarization using Transformer](https://github.com/NimraAslamkhan/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Text%20Summarization%20Using%20Transformers) |  
+| 28. | [Text Summarization using Transformer](https://github.com/NimraAslamkhan/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Text%20Summarization%20Using%20Transformers) | 29.| [Insurance Prediction Using R](https://github.com/Kushal997-das/Project-Guidance/tree/main/Machine%20Learning%20and%20Data%20Science/Basic/Insurance%20Prediction%20Using%20R) |
 <p>&nbsp;</p>
 
 ## Level 2: Intermediate 🚀