LitenAppmodel01_skl

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import seaborn as sns

# Load the CSV file containing user responses
file_path = '/content/rand_survey.csv'  # Replace with the path to your CSV file
data = pd.read_csv(file_path)

data.head()

print(data.shape)

# Define the points assigned to each response
points_dict = {
    'SomeWhat Agree': 40,
    'Agree': 45,
    'Neutral': 30,
    'Disagree': -45,
    'SomeWhat Disagree': -40
}

# Calculate total points for each user
data['Total_Points'] = data.apply(lambda row: sum(points_dict[response] for response in row[1:]), axis=1)

data

min_possible_score = sum(min(points_dict.values()) for _ in range(data.shape[1] - 1))  # Considering each question's lowest score
max_possible_score = sum(max(points_dict.values()) for _ in range(data.shape[1] - 1))  # Considering each question's highest score

print("The min value", min_possible_score)
print("The max value", max_possible_score)

# Calculate the range of possible scores
score_range = max_possible_score - min_possible_score
print(score_range)

# Calculate the step size for dividing the range into five equal parts
step_size = score_range / 5
print(step_size)

# Create and display the five equal ranges
for i in range(5):
    range_start = int(min_possible_score + (i * step_size))
    range_end = int(min_possible_score + ((i + 1) * step_size))
    print(f"Range {i+1}: {range_start} - {range_end}")

# Define personality ranges and their labels
personality_types = {
    'A': (-450, -270),
    'B': (-270, -90),
    'C': (-90, 90),
    'D': (90, 270),
    'E': (270, 450)
    # Add more personality types and their ranges as needed
}

# Function to determine personality type based on total points
def get_personality_type(score):
    for personality, (lower, upper) in personality_types.items():
        if lower <= score <= upper:
            return personality
    return 'Unknown'

# Calculate and add the personality type for each user
data['Personality_Type'] = data['Total_Points'].apply(get_personality_type)

# Display the resulting DataFrame with personality types
print(data[['Total_Points', 'Personality_Type']])

print(data.iloc[0:51])

# Count the occurrences of each personality type
personality_counts = data['Personality_Type'].value_counts()

# Display the counts of each personality type
print(personality_counts)