Project 2

1.Display the data of white wine and red wine

import pandas as pd

import numpy as np

df_red = pd.read_csv('winequality-red.csv',sep=';')

df_red

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df_white = pd.read_csv('winequality-white.csv',sep=';')

df_white

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2.Get the size of both white wine and red wine.

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3.Get the number of null in both type of wines

df_red.isnull().sum()

df_red.isnull().sum().sum()

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df_white.isnull().sum()

df_white.isnull().sum().sum()

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4.Check for duplicates and remove them in both type of wine data

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5.Get the number of uniques values in all Feature and print them

# Get the number of unique values in each feature of the white wine dataset

white_wine_unique_counts = df_white.nunique()

# Get the number of unique values in each feature of the red wine dataset

red_wine_unique_counts = df_red.nunique()

# Print the number of unique values in each feature of the red wine dataset

print("\nRed wine dataset - Number of unique values in each feature:")

print(red_wine_unique_counts)

# Print the number of unique values in each feature of the white wine dataset

print("White wine dataset - Number of unique values in each feature:")

print(white_wine_unique_counts)

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6.Print the mean density of both the sets

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7.Combine both the datasets with color of the wine in column

# Add a 'color' column to each dataset

df_red['color'] = 'red'

df_white['color'] = 'white'

# Concatenate the datasets

combined_df = pd.concat([df_white, df_red], ignore_index=True)

print(combined_df)

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8.Remove the null values if any

# Remove the null values

combined_df = combined_df.dropna()

# Print the updated dataset

print(combined_df)

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9.Remove the outliers if any

import matplotlib.pyplot as plt

# Create a box plot

plt.boxplot(combined_df)

# Show the plot

plt.show()

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combined_df.color.replace("white", 1, inplace=True)

combined_df.color.replace("red", 2, inplace=True)

# Calculate the IQR

Q1 = np.percentile(combined_df, 25)

Q3 = np.percentile(combined_df, 75)

IQR = Q3 - Q1

# Set a threshold for outliers

threshold = 1.5

# Identify outliers

lower_bound = Q1 - threshold * IQR

upper_bound = Q3 + threshold * IQR

outliers = combined_df[(combined_df < lower> upper_bound)]

# Remove outliers

combined_df = combined_df[(combined_df >= lower_bound) & (combined_df <= upper_bound)]

10.Do wines with higher alcohol content receive better ratings?

import seaborn as sns

# Check the correlation between alcohol content and ratings

correlation = combined_df['alcohol'].corr(combined_df['quality'])

# Print the correlation coefficient

print("Correlation between alcohol content and ratings:", correlation)

# Plot a scatter plot to visualize the relationship

sns.scatterplot(x='alcohol', y='quality', data=combined_df)

# Display the plot

plt.show()

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11.Find correlation between citric acid content and PH.

# Calculate the correlation between citric acid and pH

correlation = combined_df['citric acid'].corr(combined_df['pH'])

# Print the correlation coefficient

print("Correlation between citric acid content and pH:", correlation)

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12.Find regression of PH on citric acid

from sklearn.linear_model import LinearRegression

# Select the predictor (citric acid) and target variable (pH)

X = combined_df[['citric acid']]

y = combined_df['pH']

# Create an instance of the LinearRegression model

model = LinearRegression()

# Fit the model to the data

model.fit(X, y)

# Get the regression coefficients

intercept = model.intercept_

slope = model.coef_[0]

print("Regression equation : pH = {:.2f} + {:.2f} * citric acid ".format(intercept,slope))

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13.Find how many variables are independent.

# Calculate the correlation matrix

correlation_matrix = combined_df.corr()

# Count the number of independent variables

independent_count = sum((correlation_matrix < 0> -0.8))

print("Number of independent variables:", independent_count.sum())

14.Create a new column as the acidity level. Divide the existing data into 5 groups based on the acidity level. The levels are to be named ['High', 'Moderately_High', 'Medium', 'Low']. Acidity may be obtained from the PH.

# define the acidity levels

acidity_levels = {

'High': (0, 3.5),

'Moderately_High': (3.5, 3.8),

'Medium': (3.8, 4.2),

'Low': (4.2, 10)

}

# create a function and assign acidity levels based on pH

def assign_acidity_level(ph):

for level, (min_ph, max_ph) in acidity_levels.items():

if min_ph <= ph < max>

return level

# Apply the assign_acidity_level function to create the new column in both datasets:

combined_df['Acidity Level'] = combined_df['pH'].apply(assign_acidity_level)

combined_df['Acidity Level']

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15.Find the mean quality of each acidity level with groupby. Use groupby

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17.Select samples with alcohol content less than the median

median_alcohol = combined_df['alcohol'].median()

median_alcohol

# select sample

samples_below_median = combined_df[combined_df['alcohol'] < median>

samples_below_median

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18.Get the quality rating for the low alcohol and high alcohol groups.

low_alcohol_group = combined_df[combined_df['alcohol'] <= median_alcohol]

high_alcohol_group = combined_df[combined_df['alcohol'] > median_alcohol]

low_alcohol_ratings = low_alcohol_group['quality']

high_alcohol_ratings = high_alcohol_group['quality']

low_alcohol_ratings

high_alcohol_ratings

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19.Do sweeter wines get more ratings?

correlation = combined_df['residual sugar'].corr(combined_df['quality'])

correlation

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# visualize the relationship between sweetness level and wine ratings using a scatter plot

import matplotlib.pyplot as plt

plt.scatter(combined_df['residual sugar'], combined_df['quality'])

plt.xlabel('Residual Sugar')

plt.ylabel('Quality')

plt.title('Relationship between Sweetness and Wine Quality')

plt.show()

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20.Get the number of counts based on ratings and color of wine.

# Count the number of occurrences based on ratings and color

count_by_ratings_and_color = combined_df.groupby(['quality', 'color']).size()

# Print the counts

print(count_by_ratings_and_color)

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21.Is the density of red wine and white wine similar?

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So the density of both wine is almost same

22.Is the confidence interval for the differences of the proportion of white wine with rating 3 and the proportion of red wine with rating 3.

from math import sqrt

# Replace the placeholders with the actual counts and totals from your dataset

count_white_rating3 = 50 # Replace with the count of white wines with rating 3

total_white_wines = 200 # Replace with the total count of white wines

count_red_rating3 = 30 # Replace with the count of red wines with rating 3

total_red_wines = 150 # Replace with the total count of red wines

# Calculate the proportions for white wine with rating 3 and red wine with rating 3

proportion1 = count_white_rating3 / total_white_wines

proportion2 = count_red_rating3 / total_red_wines

# Calculate the sample sizes

n1 = total_white_wines

n2 = total_red_wines

standard_error = sqrt((proportion1 * (1 - proportion1) / n1) + (proportion2 * (1 - proportion2) / n2))

from scipy.stats import norm

# Set the desired confidence level (e.g., 95%)

confidence_level = 0.95

# Calculate the critical value (z-score) using the norm function from scipy.stats

critical_value = norm.ppf((1 + confidence_level) / 2)

# Calculate the margin of error

margin_of_error = critical_value * standard_error

# Calculate the lower and upper bounds of the confidence interval

lower_bound = (proportion1 - proportion2) - margin_of_error

upper_bound = (proportion1 - proportion2) + margin_of_error

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