Unlocking Student Success: Insights from a Python-Powered Deep Dive into Performance Data

As a coding enthusiast with a passion for education, I'm always eager to find ways to use my skills to better understand the factors that contribute to student success. Recently, I had the opportunity to analyze a comprehensive student performance dataset using Python, and the insights I uncovered were both fascinating and informative.

My analysis began by exploring the relationship between study time and grades. While the data revealed a general trend of students who dedicated more time to studying achieving better grades, there were some intriguing outliers. These outliers suggest that effective study habits, learning strategies, and time management skills might be just as crucial as the sheer number of hours spent with textbooks. It's not just about how much you study, but also how you study (Google, 2024).

Another compelling observation was the correlation between parental education levels and student performance. Students with more educated parents tended to achieve higher grades, highlighting the profound impact of a supportive and stimulating home environment on academic success. This underscores the importance of parental involvement in education and the potential benefits of providing additional resources and guidance to students from less advantaged backgrounds (Open AI, 2024).

The analysis also revealed some noteworthy patterns in student behavior. For instance, there was a strong positive correlation between workday alcohol consumption and weekend alcohol consumption. While moderate alcohol consumption might not be a significant detriment to academic performance in the short term, it's worth further investigation to understand its potential long-term impact on student well-being, mental health, and overall success (Open AI, 2024).

Perhaps the most significant takeaway from the data was the importance of early academic success. There was a strong positive link between performance in the first grading period and the final grade, indicating that a strong start can lay the foundation for continued achievement throughout the academic year. This highlights the critical need for early intervention programs, tutoring services, and support systems designed to help students build a solid foundation for future learning (Open AI, 2024).

Delving deeper into specific correlations, I discovered some noteworthy trends. There's a strong positive link between performance in the first grading period (G1) and the final grade (G3), indicating that early success can be a good predictor of overall academic achievement. This suggests that identifying and supporting students who struggle early on could significantly impact their overall academic trajectory (Open AI, 2024).

Interestingly, there's also a strong positive correlation between the mother's education level (Medu) and the father's education level (Fedu). This could indicate that individuals with similar education levels tend to partner together. While this correlation might not have a direct impact on student performance, it provides an interesting glimpse into the social dynamics that might influence a student's home environment (Google, 2024).

Another notable correlation is the strong positive association between workday alcohol consumption (Dalc) and weekend alcohol consumption (Walc). Students who consume more alcohol on weekdays are also likely to consume more on weekends. This correlation could be valuable for identifying students who might be at risk of developing unhealthy alcohol consumption patterns (Open AI, 2024).

These are just a few of the insights I gained from this data exploration. It's evident that student success is a multifaceted issue influenced by a complex interplay of academic, social, and personal factors. By harnessing the power of coding and data analysis, we can gain a deeper understanding of these factors and develop more effective, targeted strategies to support students in reaching their full potential.

#DataAnalysis #Python #StudentSuccess #Education #Insights

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References

1. Google. (2024, November 15). Coding partner: Level up your coding skills [Chat session]. Gemini Advanced. Retrieved from?https://gemini.google.com/gem/coding-partner/4679628bde181683
2. Open AI. (2024). ChatGPT. chatgpt.com?

Here's a copy of the files if you feel like exploring them too:

https://archive.ics.uci.edu/dataset/320/student+performance

Here's the python coding used to obtain the above insights if you'd like to try it out for yourself, I recommend using Google Colab:

?import pandas as pd

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# Read the CSV files into Pandas DataFrames

student_mat_df = pd.read_csv("student-mat.csv", delimiter=";")

student_por_df = pd.read_csv("student-por.csv", delimiter=";")

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# Display the first 5 rows of each DataFrame

print("student-mat.csv head:")

print(student_mat_df.head().to_markdown(index=False, numalign="left", stralign="left"))

print("\nstudent-por.csv head:")

print(student_por_df.head().to_markdown(index=False, numalign="left", stralign="left"))

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# Print the column names and their data types

print("\nstudent-mat.csv info:")

student_mat_df.info()

print("\nstudent-por.csv info:")

student_por_df.info()

# Check if the first row in each dataset represents a header

def check_header(df):

? ? """

? ? Checks if the first row of a DataFrame is a header.

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? ? Args:

? ? ? df: The DataFrame to check.

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? ? Returns:

? ? ? A string indicating whether the first row is a header.

? ? """

? ? first_row = df.iloc[0]

? ? column_names = df.columns

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? ? # Check if the first row contains any non-string values

? ? if not all(isinstance(value, str) for value in first_row):

? ? ? ? return "The first row is not a header - it contains non-string values."

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? ? # Check if the first row is identical to the column names

? ? if (first_row == column_names).all():

? ? ? ? return "The first row is not a header - it is identical to the column names."

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? ? # Check if the first row contains any values that look like column names

? ? column_name_set = set(column_names)

? ? if any(value in column_name_set for value in first_row):

? ? ? ? return "The first row is not a header - it contains values that look like column names."

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? ? # If none of the above conditions are met, assume the first row is a header

? ? return "The first row is a header"

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# Check the headers for both DataFrames

print(f"student-mat.csv: {check_header(student_mat_df)}")

print(f"student-por.csv: {check_header(student_por_df)}")

# Read the CSV files into Pandas DataFrames, specifying that the first row contains the column names

student_mat_df = pd.read_csv("student-mat.csv", delimiter=";", header=0)

student_por_df = pd.read_csv("student-por.csv", delimiter=";", header=0)

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# Display the first 5 rows of each DataFrame

print("student-mat.csv head:")

print(student_mat_df.head().to_markdown(index=False, numalign="left", stralign="left"))

print("\nstudent-por.csv head:")

print(student_por_df.head().to_markdown(index=False, numalign="left", stralign="left"))

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# Print the column names and their data types

print("\nstudent-mat.csv info:")

student_mat_df.info()

print("\nstudent-por.csv info:")

student_por_df.info()

# Merge the two datasets based on the common columns

common_columns = student_mat_df.columns.intersection(student_por_df.columns)

merged_df = pd.merge(student_mat_df, student_por_df, on=list(common_columns), how='outer')

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# Fill missing values in numerical columns with the median

numerical_columns = merged_df.select_dtypes(include=['number']).columns

for col in numerical_columns:

? ? merged_df[col] = merged_df[col].fillna(merged_df[col].median())

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# Fill missing values in categorical columns with the mode

categorical_columns = merged_df.select_dtypes(include=['object']).columns

for col in categorical_columns:

? ? mode_value = merged_df[col].mode()[0]

? ? merged_df[col] = merged_df[col].fillna(mode_value)

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# Print the number of rows in the merged dataset

print(f"Number of rows in the merged dataset: {len(merged_df)}")

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import pandas as pd

import altair as alt

import warnings

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# Suppress FutureWarnings

warnings.simplefilter(action='ignore', category=FutureWarning)

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# ... (rest of the code remains the same)

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# Print the summary statistics of the merged DataFrame

print(merged_df.describe().to_markdown(numalign="left", stralign="left"))

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# Print the number of unique values for each column

print("\nNumber of unique values for each column:")

print(merged_df.nunique().to_markdown(numalign="left", stralign="left"))

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# Create visualizations for each column

for col in merged_df.columns:

? ? if merged_df[col].nunique() <= 50:

? ? ? ? # Bar plot for categorical variables

? ? ? ? chart = alt.Chart(merged_df).mark_bar().encode(

? ? ? ? ? ? x=col,

? ? ? ? ? ? y='count()',

? ? ? ? ? ? tooltip=[col, 'count()']

? ? ? ? ).properties(

? ? ? ? ? ? title=f"Distribution of {col}"

? ? ? ? ).interactive()

? ? else:

? ? ? ? # Histogram for numerical variables

? ? ? ? chart = alt.Chart(merged_df).mark_bar().encode(

? ? ? ? ? ? alt.X(col, bin=True),

? ? ? ? ? ? y='count()',

? ? ? ? ? ? tooltip=[alt.Tooltip(col, bin=True), 'count()']

? ? ? ? ).properties(

? ? ? ? ? ? title=f"Histogram of {col}"

? ? ? ? ).interactive()

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? ? # Display the chart in Google Colab

? ? chart.display()

#Cross-referencing for possible additional insights on OpenAI # Step 1: Import necessary libraries

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

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# Enable inline plots for Google Colab

%matplotlib inline

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# Step 2: Load the 'student-por.csv' dataset

# Update the file path if necessary when uploading to Google Colab

file_path = '/content/student-por.csv' ?# Change this path if needed

student_data = pd.read_csv(file_path, sep=';')

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# Step 3: Display basic information about the dataset

print("Dataset Overview:")

print(student_data.info())

print("\nFirst 5 Rows:")

print(student_data.head())

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# Step 4: Generate descriptive statistics for numerical features

print("\nDescriptive Statistics:")

print(student_data.describe())

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# Step 5: Check for missing values

missing_values = student_data.isnull().sum()

print("\nMissing Values:")

print(missing_values[missing_values > 0])

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# Step 6: Visualize the distribution of final grades (G3)

plt.figure(figsize=(10, 5))

sns.histplot(student_data['G3'], bins=20, kde=True, color='skyblue')

plt.title('Distribution of Final Grades (G3)')

plt.xlabel('Final Grade (G3)')

plt.ylabel('Frequency')

plt.show()

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# Step 7: Visualize the relationship between study time and final grade

plt.figure(figsize=(10, 5))

sns.boxplot(x='studytime', y='G3', data=student_data, palette='Set2')

plt.title('Study Time vs Final Grade')

plt.xlabel('Weekly Study Time (1: <2h, 2: 2-5h, 3: 5-10h, 4: >10h)')

plt.ylabel('Final Grade (G3)')

plt.show()

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# Step 8: Correlation heatmap of numerical features

plt.figure(figsize=(12, 8))

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# Select only numerical columns for correlation

numeric_data = student_data.select_dtypes(include=['number'])

correlation_matrix = numeric_data.corr()

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# Plot the heatmap

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)

plt.title('Correlation Heatmap of Numerical Features')

plt.show()

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# Step 9: Analyze the impact of parental education on final grades

plt.figure(figsize=(10, 5))

sns.boxplot(x='Medu', y='G3', data=student_data, palette='muted')

plt.title("Mother's Education Level vs Final Grade")

plt.xlabel("Mother's Education Level (0: None, 4: Higher Education)")

plt.ylabel('Final Grade (G3)')

plt.show()

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# Step 10: Save cleaned and analyzed data (optional)

# student_data.to_csv('/content/student_data_cleaned.csv', index=False)

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