Profile of Student Sleep Patterns Dataset

Oct 30, 2024, 4:43 AM

student_sleep_patterns.csv

Missing file

give me the profile of my data

The dataset contains 500 entries and 14 columns. Here is a brief overview of the dataset:
  • Columns and Data Types:
    • Student_ID: Integer
    • Age: Integer
    • Gender: Object (Categorical)
    • University_Year: Object (Categorical)
    • Sleep_Duration: Float
    • Study_Hours: Float
    • Screen_Time: Float
    • Caffeine_Intake: Integer
    • Physical_Activity: Integer
    • Sleep_Quality: Integer
    • Weekday_Sleep_Start: Float
    • Weekend_Sleep_Start: Float
    • Weekday_Sleep_End: Float
    • Weekend_Sleep_End: Float
  • Summary Statistics:
    • The average Sleep_Duration is approximately 7.5 hours.
    • The average Study_Hours is approximately 6.5 hours.
    • The average Screen_Time is approximately 3.0 hours.
    • The average Caffeine_Intake is 3 units.
    • The average Physical_Activity score is 50.
    • The average Sleep_Quality score is 5.
The dataset provides a comprehensive view of students' sleep patterns, study habits, and lifestyle factors. If you have any specific analysis or visualization in mind, feel free to ask!

build a model and evalute it

To build and evaluate a model, we first need to define our target variable and the features we want to use for prediction. Let's assume we want to predict Sleep_Quality based on other features like Sleep_Duration, Study_Hours, Screen_Time, Caffeine_Intake, and Physical_Activity.
We will follow these steps:
  1. Preprocess the data (handle categorical variables, missing values, etc.).
  2. Split the data into training and testing sets.
  3. Train a machine learning model (e.g., Random Forest).
  4. Evaluate the model using appropriate metrics (e.g., accuracy, precision, recall).
Let's start by preprocessing the data.
The Random Forest model was trained to predict Sleep_Quality using features such as Sleep_Duration, Study_Hours, Screen_Time, Caffeine_Intake, and Physical_Activity. Here are the evaluation results:
  • Accuracy: The model achieved an accuracy of 8% on the test set.
  • Classification Report:
    • The precision, recall, and f1-score for each class (1 to 10) are quite low, indicating that the model is not performing well in distinguishing between different sleep quality levels.
    • The macro average and weighted average scores are also low, reflecting the overall poor performance of the model.
The low accuracy and poor classification metrics suggest that the model is not effectively capturing the patterns in the data. This could be due to several reasons, such as:
  • The features selected may not be strong predictors of sleep quality.
  • The dataset might be imbalanced, leading to biased predictions.
  • The model parameters may need tuning.
Further analysis and feature engineering might be necessary
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