Project 1

pdf link same ans : https://docs.google.com/document/d/16zyPpAYnQDP_nqj_5xwpSOKR5JDQiNDM/edit?usp=sharing&ouid=112031055003736671606&rtpof=true&sd=true

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1. Explain the data. Check It is supervised or Unsupervised. Demonstrate?

The provided data is an example of supervised learning data. In supervised learning, the algorithm is trained on a labeled dataset, where the input features and their corresponding output (or target) values are provided

n this dataset:

• Input Features (Independent Variables):

1. age
2. sex
3. bmi
4. children
5. smoker
6. region

• Output (Dependent Variable):
• 7. charges

Info of data

1. age: Age of the individual.
2. sex: Gender of the individual (female/male).
3. BMI: Body Mass Index (a measure of body fat based on height and weight).
4. children: Number of children or dependents covered by the insurance.
5. smoker: Smoking status (yes/no).
6. region: Geographic region of the individual (southwest, southeast, northwest, northeast).
7. charges: Insurance charges associated with the individual.

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2. Estimate the minimum sample size n to get 99% accurate predictions. (precision = 0.02)

To estimate the minimum sample size required for a desired level of precision in predictions, you can use the formula for sample size in margin of error estimation. The formula is:

n = ceil((z_score**2 * p * (1 - p)) / E**2)

Where:

n is the sample size.

Z is the Z-score corresponding to the desired confidence level (99% confidence corresponds to a Z-score of approximately 2.576).

p is the estimated probability of success (0.5 for maximum uncertainty, resulting in the maximum sample size).

E is the desired margin of error (precision).

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3. Check the data is cleaned or not. If not then clean it (Null values, Row/Column Duplicates, Outliers, Change the string into numbers)

1. Null values

NO NULL Values

2. Row/Column Duplicates

3. Change the string into numbers

4. Outliers

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• The chi-squared test statistic: 0.2640803541964238
• P-value: 0.6073306531193106
• Degrees of freedom: 1

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5. Check that all regressor variables (independent variable) are independent of each other or not.

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6. Check the dependency between response and regressors.

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7. Predict the regression Line to predict the charges for insurance using independent variables.

Predicted Charges: $77,449.03

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8. Predict the accuracy of the regression Model.

In summary, the regression model's performance is moderate:

• R-squared (R²): 0.3003 (indicating 30.03% of variance explained)
• Mean Squared Error (MSE): 20783778.86
• Mean Absolute Error (MAE): 3097.37

These metrics provide a measure of how well the model predicts insurance charges based on the given independent variables.

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9. Predict insurance charge for Age = 29, Sex = F, bmi = 28, children = 1, Smoke = Yes, region = southeast.

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10. Give the percentage of error in regression model.

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11. Give the 95% confidence interval for average charge insurance.

*************************************** Thank You *******************************************