Task #1 - Prediction using Supervised ML

I was asked to predict the percentage of a student based on the number of study hours. I used a simple linear regression model to build the prediction model. The model was able to predict the score with an accuracy of 95%. I am happy with the results of the model, and I am confident that I can use this knowledge to solve other data science problems.

Let's Deep Dive ??

Let's understand first How Supervised Machine Learning Works.

Supervised ML learning is the type of machine learning in which machines are trained using well "labeled" training data, and on the basis of that data, machines predict the output. The labeled data means some input data is already tagged with the correct output.

Let's understand how machine learning can predict student success.

Dataset

• The dataset we will be using in this task is available here: https://bit.ly/w-data

Problem Statement

• Predict the percentage of a student based on the number of study hours.
• What will be the predicted score if a student studies for 9.25 hours/day?

Easy Explanation

1)???? Data Acquisition:

• First, load the dataset from this https://bit.ly/w-data URL. ?For loading a dataset some libraries are necessary to import like Pandas and NumPy.
• The dataset contains 2 columns/Attributes: o Hours:?The number of study hours

o Score:?The student's score

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2)???? Data Preprocessing:

• The Data Preprocessing step is most important for ensuring that the data is in a format that can be used by the ML model. This includes removing outliers, imputing missing values, and transforming the data into a format that is consistent with the features of the model.
• The dataset is clean and does not contain any outliers or missing values. Therefore, we do not need to perform any data preprocessing.

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3)???? Data Visualization:

• Data visualization is used for understanding the data and identifying patterns. This can be helpful in the data preprocessing step and in the model-building step.
• Create graph-like scatter plots to visualize the relationship between Hours and Score. This will help you to confirm whether a linear relationship exists.

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4)???? Model Building:

• Model Building?is the process of creating a model that can learn from the data and make predictions. For building a model, first splitting the data into training and testing sets.
• Commonly, the split ratio is 80% for training and 20% for testing, but you can adjust it as needed.
• Select simple Linear Regression for building a model, because we have only 2 columns.?

5) Model Evaluation:

• Once the model is trained, we need to evaluate its performance on the testing set. This will give us an idea of how well the model will generalize to new data.
• It is done by comparing the predictions of the model to the actual values.

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6)???? Prediction:

• Now that we have a trained and evaluated model, we can use it to make predictions on new data. For example, we can predict the score of a student who studies for 9.25 hours per day.
• The predicted score is 92.39. This means that the model predicts that the student will score 92% if they study for 9.25 hours per day.

?Video Demonstration

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