final.py

# ============================================================
# STUDENT PERFORMANCE ANALYSIS SYSTEM (Filters + Visualization)
# ============================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# -------------------------------
# LOAD DATASET
# -------------------------------
df = pd.read_csv("student_performance_6000.csv")
df["Average"] = df.loc[:, "Math":"Computer"].mean(axis=1)

filtered_df = df.copy()

# Active filters memory
active_filters = {
    "gender": None,
    "age": None,
    "id": None,
}


# ============================================================
# APPLY FILTERS TO DATASET
# ============================================================
def apply_all_filters():
    global filtered_df
    temp = df.copy()

    if active_filters["gender"]:
        temp = temp[temp["Gender"] == active_filters["gender"]]

    if active_filters["age"]:
        min_age, max_age = active_filters["age"]
        temp = temp[(temp["Age"] >= min_age) & (temp["Age"] <= max_age)]

    if active_filters["id"]:
        start_id, end_id = active_filters["id"]
        temp = temp[(temp["Student_ID"] >= start_id) & (temp["Student_ID"] <= end_id)]

    filtered_df = temp


# ============================================================
# WELCOME SCREEN
# ============================================================
def show_welcome_screen():
    print("----------------------------------------")
    print("        WELCOME TO MY PROJECT         ")
    print("----------------------------------------")
    print("Topic       : Student Performance Analysis System")
    print("Submitted To: Prof. Shubham Verma")
    print("Submitted By: Jadwinder Singh")
    print("Course      : MSc IT 1st Semester")
    print("Subject     : Data Analysis (MS-71)")
    print("----------------------------------------\n")


# ============================================================
# BASIC MENU FUNCTIONS
# ============================================================
def show_introduction():
    print("\n===== INTRODUCTION =====")
    print("This project analyzes student performance using Python, Pandas, and NumPy.")
    print("You can filter data and view interactive visualizations.\n")


def show_dataset():
    print("\n===== FILTERED DATASET =====")
    print(filtered_df)


def show_average_marks():
    print("\n===== AVERAGE MARKS =====")
    print(filtered_df[["Name", "Average"]])


def show_top_and_bottom():
    if filtered_df.empty:
        print("\nNo data available!")
        return

    top = filtered_df.loc[filtered_df["Average"].idxmax()]
    low = filtered_df.loc[filtered_df["Average"].idxmin()]

    print("\n===== TOP PERFORMER =====")
    print(f"{top['Name']} | Avg = {top['Average']:.2f}")

    print("\n===== LOWEST PERFORMER =====")
    print(f"{low['Name']} | Avg = {low['Average']:.2f}")


def show_subject_average():
    if filtered_df.empty:
        print("\nNo data available!")
        return

    print("\n===== SUBJECT-WISE AVERAGE =====")
    print(filtered_df.loc[:, "Math":"Computer"].mean())


def show_class_summary():
    if filtered_df.empty:
        print("\nNo data available!")
        return

    print("\n===== CLASS SUMMARY =====")
    print(f"Class Average: {filtered_df['Average'].mean():.2f}")
    print(f"Highest Avg: {filtered_df['Average'].max():.2f}")
    print(f"Lowest Avg: {filtered_df['Average'].min():.2f}")


def show_above_class_average():
    if filtered_df.empty:
        print("\nNo data!")
        return

    cavg = filtered_df["Average"].mean()
    above = filtered_df[filtered_df["Average"] > cavg]

    print("\n===== STUDENTS ABOVE CLASS AVERAGE =====")
    print(above[["Name", "Average"]])


# ============================================================
# FILTER MENU (BEFORE VISUALIZATION)
# ============================================================
def visualization_filter_menu():
    global active_filters

    while True:
        print("\n===== VISUALIZATION FILTER OPTIONS =====")
        print("1. No Filter (Show All)")
        print("2. Filter by Gender")
        print("3. Filter by Age Range")
        print("4. Filter by Student ID Range")
        print("5. Apply Multiple Filters")
        print("6. Reset All Filters")
        print("7. Start Visualization")

        choice = input("Enter choice: ")

        if choice == "1":
            active_filters = {"gender": None, "age": None, "id": None}
            apply_all_filters()
            print("✔ Showing ALL students.")

        elif choice == "2":
            g = input("Enter Gender (Male/Female/Other): ").title()
            active_filters["gender"] = g
            apply_all_filters()
            print(f"✔ Filter Applied: Gender = {g}")

        elif choice == "3":
            try:
                a = int(input("Enter Min Age: "))
                b = int(input("Enter Max Age: "))
                active_filters["age"] = (a, b)
                apply_all_filters()
                print(f"✔ Age Filter Applied: {a}-{b}")
            except:
                print("❌ Invalid Age Input")

        elif choice == "4":
            try:
                s = int(input("Enter Start ID: "))
                e = int(input("Enter End ID: "))
                active_filters["id"] = (s, e)
                apply_all_filters()
                print(f"✔ ID Filter: {s}-{e}")
            except:
                print("❌ Invalid ID Input")

        elif choice == "5":
            print("Enter values (press Enter to skip):")

            g = input("Gender: ").title()
            if g: active_filters["gender"] = g

            try:
                a = input("Min Age: "); b = input("Max Age: ")
                if a and b:
                    active_filters["age"] = (int(a), int(b))
            except: pass

            try:
                s = input("Start ID: "); e = input("End ID: ")
                if s and e:
                    active_filters["id"] = (int(s), int(e))
            except: pass

            apply_all_filters()
            print("✔ Multiple Filters Applied")

        elif choice == "6":
            active_filters = {"gender": None, "age": None, "id": None}
            apply_all_filters()
            print("✔ Filters Reset")

        elif choice == "7":
            print("\nStarting Visualization...")
            apply_all_filters()
            break

        else:
            print("❌ Invalid option")


# ============================================================
# VISUALIZATION WITH n / p / q
# ============================================================
def show_visualization():
    if filtered_df.empty:
        print("\nNo filtered records to visualize!")
        return

    total_graphs = 4
    graph_index = 0

    plt.close("all")

    fig, ax = plt.subplots(figsize=(9, 5))

    # -------------------------------
    # DRAW GRAPH
    # -------------------------------
    def plot_graph(i):
        ax.clear()

        if i == 0:
            ax.bar(filtered_df["Name"], filtered_df["Average"], color="skyblue")
            ax.set_title("Average Marks")
            ax.tick_params(axis="x", rotation=90, labelsize=6)

        elif i == 1:
            filtered_df.loc[:, "Math":"Computer"].mean().plot(kind="bar", ax=ax)
            ax.set_title("Subject-wise Average")

        elif i == 2:
            ax.hist(filtered_df["Attendance(%)"], bins=10, edgecolor="black")
            ax.set_title("Attendance Distribution (Histogram)")
            ax.set_xlabel("Attendance %")
            ax.set_ylabel("Number of Students")


        elif i == 3:
            ax.violinplot([filtered_df["Math"], filtered_df["Science"], filtered_df["English"],
                        filtered_df["History"], filtered_df["Computer"]],
                        showmeans=True)
            ax.set_xticks([1,2,3,4,5])
            ax.set_xticklabels(["Math","Science","English","History","Computer"])
            ax.set_title("Subject-wise Marks Distribution (Violin Plot)")
        fig.canvas.draw_idle()

    # -------------------------------
    # KEYBOARD CONTROLS
    # -------------------------------
    def on_key(event):
        nonlocal graph_index

        if event.key == "n":       # Next
            graph_index = (graph_index + 1) % total_graphs
            plot_graph(graph_index)

        elif event.key == "p":     # Previous
            graph_index = (graph_index - 1) % total_graphs
            plot_graph(graph_index)

        elif event.key == "q":     # Quit
            plt.close("all")

    fig.canvas.mpl_connect("key_press_event", on_key)

    plot_graph(graph_index)
    plt.show()


# ============================================================
# MAIN MENU
# ============================================================
show_welcome_screen()

while True:
    print("\n==============================")
    print(" STUDENT PERFORMANCE ANALYSIS ")
    print("==============================")
    print("1. Introduction")
    print("2. Show Dataset")
    print("3. Show Average Marks")
    print("4. Top & Bottom Performer")
    print("5. Subject-wise Average")
    print("6. Class Summary")
    print("7. Students Above Class Average")
    print("8. Save Output CSV")
    print("9. Visualize Data with ")
    print("10. Exit")
    print("==============================")

    ch = input("Enter your choice: ")

    if ch == "1": show_introduction()
    elif ch == "2": show_dataset()
    elif ch == "3": show_average_marks()
    elif ch == "4": show_top_and_bottom()
    elif ch == "5": show_subject_average()
    elif ch == "6": show_class_summary()
    elif ch == "7": show_above_class_average()
    elif ch == "8":
        filtered_df.to_csv("filtered_output.csv", index=False)
        print("✔ Saved as filtered_output.csv")
    elif ch == "9":
        visualization_filter_menu()
        show_visualization()
    elif ch == "10":
        print("Exiting... Thank you!")
        break
    else:
        print("❌ Invalid Choice")

# ============================================================
# STUDENT PERFORMANCE ANALYSIS SYSTEM (Filters + Visualization)
# ============================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# -------------------------------
# LOAD DATASET
# -------------------------------
df = pd.read_csv("student_performance_6000.csv")
df["Average"] = df.loc[:, "Math":"Computer"].mean(axis=1)

filtered_df = df.copy()

# Active filters memory
active_filters = {
    "gender": None,
    "age": None,
    "id": None,
}


# ============================================================
# APPLY FILTERS TO DATASET
# ============================================================
def apply_all_filters():
    global filtered_df
    temp = df.copy()

    if active_filters["gender"]:
        temp = temp[temp["Gender"] == active_filters["gender"]]

    if active_filters["age"]:
        min_age, max_age = active_filters["age"]
        temp = temp[(temp["Age"] >= min_age) & (temp["Age"] <= max_age)]

    if active_filters["id"]:
        start_id, end_id = active_filters["id"]
        temp = temp[(temp["Student_ID"] >= start_id) & (temp["Student_ID"] <= end_id)]

    filtered_df = temp


# ============================================================
# WELCOME SCREEN
# ============================================================
def show_welcome_screen():
    print("----------------------------------------")
    print("        WELCOME TO MY PROJECT         ")
    print("----------------------------------------")
    print("Topic       : Student Performance Analysis System")
    print("Submitted To: Prof. Shubham Verma")
    print("Submitted By: Jadwinder Singh")
    print("Course      : MSc IT 1st Semester")
    print("Subject     : Data Analysis (MS-71)")
    print("----------------------------------------\n")


# ============================================================
# BASIC MENU FUNCTIONS
# ============================================================
def show_introduction():
    print("\n===== INTRODUCTION ABOUT THE PROJECT =====\n")
    print("The 'Student Performance Analysis System' is a Python-based project developed using the Pandas and NumPy libraries.")
    print("It analyzes and evaluates the academic performance of students based on their marks and attendance records.\n")
    print("The system performs data operations such as:")
    print("- Calculating student-wise averages")
    print("- Identifying top and bottom performers")
    print("- Computing subject-wise averages")
    print("- Displaying students who scored above the class average\n")
    print("This project helps teachers and administrators gain insights into overall class performance and demonstrates how Python can be used effectively for data analysis.\n")
    print("The system is completely offline, simple, and user-friendly.\n")



def show_dataset():
    print("\n===== FILTERED DATASET =====")
    print(filtered_df)


def show_average_marks():
    print("\n===== AVERAGE MARKS =====")
    print(filtered_df[["Name", "Average"]])


def show_top_and_bottom():
    if filtered_df.empty:
        print("\nNo data available!")
        return

    top = filtered_df.loc[filtered_df["Average"].idxmax()]
    low = filtered_df.loc[filtered_df["Average"].idxmin()]

    print("\n===== TOP PERFORMER =====")
    print(f"{top['Name']} | Avg = {top['Average']:.2f}")

    print("\n===== LOWEST PERFORMER =====")
    print(f"{low['Name']} | Avg = {low['Average']:.2f}")


def show_subject_average():
    if filtered_df.empty:
        print("\nNo data available!")
        return

    print("\n===== SUBJECT-WISE AVERAGE =====")
    print(filtered_df.loc[:, "Math":"Computer"].mean())


def show_class_summary():
    if filtered_df.empty:
        print("\nNo data available!")
        return

    print("\n===== CLASS SUMMARY =====")
    print(f"Class Average: {filtered_df['Average'].mean():.2f}")
    print(f"Highest Avg: {filtered_df['Average'].max():.2f}")
    print(f"Lowest Avg: {filtered_df['Average'].min():.2f}")


def show_above_class_average():
    if filtered_df.empty:
        print("\nNo data!")
        return

    cavg = filtered_df["Average"].mean()
    above = filtered_df[filtered_df["Average"] > cavg]

    print("\n===== STUDENTS ABOVE CLASS AVERAGE =====")
    print(above[["Name", "Average"]])


# ============================================================
# FILTER MENU (BEFORE VISUALIZATION)
# ============================================================
def visualization_filter_menu():
    global active_filters

    while True:
        print("\n===== VISUALIZATION FILTER OPTIONS =====")
        print("1. No Filter (Show All)")
        print("2. Filter by Gender")
        print("3. Filter by Age Range")
        print("4. Filter by Student ID Range")
        print("5. Apply Multiple Filters")
        print("6. Reset All Filters")
        print("7. Start Visualization")

        choice = input("Enter choice: ")

        if choice == "1":
            active_filters = {"gender": None, "age": None, "id": None}
            apply_all_filters()
            print("✔ Showing ALL students.")

        elif choice == "2":
            g = input("Enter Gender (Male/Female/Other): ").title()
            active_filters["gender"] = g
            apply_all_filters()
            print(f"✔ Filter Applied: Gender = {g}")

        elif choice == "3":
            try:
                a = int(input("Enter Min Age: "))
                b = int(input("Enter Max Age: "))
                active_filters["age"] = (a, b)
                apply_all_filters()
                print(f"✔ Age Filter Applied: {a}-{b}")
            except:
                print("❌ Invalid Age Input")

        elif choice == "4":
            try:
                s = int(input("Enter Start ID: "))
                e = int(input("Enter End ID: "))
                active_filters["id"] = (s, e)
                apply_all_filters()
                print(f"✔ ID Filter: {s}-{e}")
            except:
                print("❌ Invalid ID Input")

        elif choice == "5":
            print("Enter values (press Enter to skip):")

            g = input("Gender: ").title()
            if g: active_filters["gender"] = g

            try:
                a = input("Min Age: "); b = input("Max Age: ")
                if a and b:
                    active_filters["age"] = (int(a), int(b))
            except: pass

            try:
                s = input("Start ID: "); e = input("End ID: ")
                if s and e:
                    active_filters["id"] = (int(s), int(e))
            except: pass

            apply_all_filters()
            print("✔ Multiple Filters Applied")

        elif choice == "6":
            active_filters = {"gender": None, "age": None, "id": None}
            apply_all_filters()
            print("✔ Filters Reset")

        elif choice == "7":
            print("\nStarting Visualization...")
            apply_all_filters()
            break

        else:
            print("❌ Invalid option")


# ============================================================
# VISUALIZATION WITH n / p / q
# ============================================================
def show_visualization():
    if filtered_df.empty:
        print("\nNo filtered records to visualize!")
        return

    total_graphs = 4
    graph_index = 0

    plt.close("all")

    fig, ax = plt.subplots(figsize=(9, 5))

    # -------------------------------
    # DRAW GRAPH
    # -------------------------------
    def plot_graph(i):
        ax.clear()

        if i == 0:
            ax.bar(filtered_df["Name"], filtered_df["Average"], color="skyblue")
            ax.set_title("Average Marks")
            ax.tick_params(axis="x", rotation=90, labelsize=6)

        elif i == 1:
            filtered_df.loc[:, "Math":"Computer"].mean().plot(kind="bar", ax=ax)
            ax.set_title("Subject-wise Average")

        elif i == 2:
            ax.hist(filtered_df["Attendance(%)"], bins=10, edgecolor="black")
            ax.set_title("Attendance Distribution (Histogram)")
            ax.set_xlabel("Attendance %")
            ax.set_ylabel("Number of Students")


        elif i == 3:
            ax.violinplot([filtered_df["Math"], filtered_df["Science"], filtered_df["English"],
                        filtered_df["History"], filtered_df["Computer"]],
                        showmeans=True)
            ax.set_xticks([1,2,3,4,5])
            ax.set_xticklabels(["Math","Science","English","History","Computer"])
            ax.set_title("Subject-wise Marks Distribution (Violin Plot)")
        fig.canvas.draw_idle()

    # -------------------------------
    # KEYBOARD CONTROLS
    # -------------------------------
    def on_key(event):
        nonlocal graph_index

        if event.key == "n":       # Next
            graph_index = (graph_index + 1) % total_graphs
            plot_graph(graph_index)

        elif event.key == "p":     # Previous
            graph_index = (graph_index - 1) % total_graphs
            plot_graph(graph_index)

        elif event.key == "q":     # Quit
            plt.close("all")

    fig.canvas.mpl_connect("key_press_event", on_key)

    plot_graph(graph_index)
    plt.show()


# ============================================================
# MAIN MENU
# ============================================================
show_welcome_screen()

while True:
    print("\n==============================")
    print(" STUDENT PERFORMANCE ANALYSIS ")
    print("==============================")
    print("1. Introduction")
    print("2. Show Dataset")
    print("3. Show Average Marks")
    print("4. Top & Bottom Performer")
    print("5. Subject-wise Average")
    print("6. Class Summary")
    print("7. Students Above Class Average")
    print("8. Save Output CSV")
    print("9. Visualize Data with ")
    print("10. Exit")
    print("==============================")

    ch = input("Enter your choice: ")

    if ch == "1": show_introduction()
    elif ch == "2": show_dataset()
    elif ch == "3": show_average_marks()
    elif ch == "4": show_top_and_bottom()
    elif ch == "5": show_subject_average()
    elif ch == "6": show_class_summary()
    elif ch == "7": show_above_class_average()
    elif ch == "8":
        filtered_df.to_csv("filtered_output.csv", index=False)
        print("✔ Saved as filtered_output.csv")
    elif ch == "9":
        visualization_filter_menu()
        show_visualization()
    elif ch == "10":
        print("Exiting... Thank you!")
        break
    else:
        print("❌ Invalid Choice")