What Is Logistic Regression?
Logistic regression is a supervised machine learning algorithm used for binary classification — predicting whether an outcome belongs to one of two classes (e.g., survived / did not survive, spam / not spam, fraud / not fraud).
Unlike linear regression, which predicts a continuous value, logistic regression predicts a probability that an observation belongs to the positive class. That probability is then converted into a class label using a decision threshold (typically 0.5).
Use Cases
Logistic regression is widely used when the target is binary or can be treated as binary:
| Domain | Example |
|---|---|
| Healthcare | Disease diagnosis (positive / negative test result) |
| Finance | Credit default prediction, fraud detection |
| Marketing | Customer churn, click-through prediction |
| HR | Employee attrition prediction |
| Transportation | Passenger survival, accident severity classification |
In this notebook, we use logistic regression to predict whether a Titanic passenger survived (1) or not (0) based on features such as age, gender, fare, and passenger class.
The Model Equation
Logistic regression starts with a linear combination of features (similar to linear regression):
z = β₀ + β₁x₁ + β₂x₂ + … + βₙxₙ
This value z is passed through the sigmoid (logistic) function to produce a probability between 0 and 1:
P(y = 1 | x) = σ(z) = 1 / (1 + e^(-z))
Where:
- P(y=1 | x) = probability of the positive class
- β₀ = intercept (bias term)
- β₁, β₂, …, βₙ = coefficients (weights) for each feature
- x₁, x₂, …, xₙ = input feature values
The sigmoid function squashes any real number into the range (0, 1), making it ideal for probability estimation.
Log-Odds (Logit)
Instead of modeling probability directly, logistic regression can be understood as modeling the log-odds (also called the logit) of the outcome:
logit(p) = ln(p / (1 – p)) = β₀ + β₁x₁ + β₂x₂ + … + βₙxₙ
Where:
- p = probability of the positive class
- p / (1-p) = odds (ratio of probability of success to probability of failure)
- ln(p / (1-p)) = log-odds or logit
Key insight: The logit transforms a probability (bounded 0–1) into an unbounded real number, allowing us to use a linear model. A one-unit increase in a feature changes the log-odds by that feature’s coefficient βⱼ.
Example: If β_gender = 1.5, being in the coded “female” category increases the log-odds of survival by 1.5 — which corresponds to multiplying the odds by e^1.5 ≈ 4.48.
Algorithm & Optimization
Logistic regression learns the coefficients β₀, β₁, …, βₙ by maximizing the likelihood of observing the actual training labels — equivalent to minimizing the log-loss (cross-entropy loss):
L = -(1/N) × Σ [ yᵢ log(p̂ᵢ) + (1 – yᵢ) log(1 – p̂ᵢ) ]
Where:
- N = number of training samples
- yᵢ = actual label (0 or 1) for sample i
- p̂ᵢ = predicted probability for sample i
How optimization works:
- Initialize coefficients (often to zero or small random values)
- Compute predicted probabilities using the sigmoid function
- Calculate the loss (log-loss) across all training samples
- Compute the gradient (partial derivatives of loss w.r.t. each coefficient)
- Update coefficients using an iterative solver such as:
- L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) — default in scikit-learn
- Stochastic Gradient Descent (SGD)
- Newton’s method
- Repeat until convergence (loss stops improving)
scikit-learn’s LogisticRegression() uses these solvers under the hood — no manual gradient descent is needed.
Underlying Assumptions
Logistic regression performs best when these assumptions are reasonably met:
| Assumption | Description |
|---|---|
| Binary outcome | The dependent variable has only two meaningful classes |
| Independence of observations | Each row is independent (no repeated measures on the same subject) |
| Linearity of log-odds | The log-odds of the outcome is a linear function of the predictors |
| No perfect multicollinearity | Predictors should not be perfectly correlated with each other |
| Large enough sample size | Rule of thumb: at least 10 events per predictor variable |
| No extreme outliers | Outliers can disproportionately influence coefficient estimates |
Violations don’t always make the model unusable, but they can reduce accuracy or make coefficients harder to interpret.
Interpreting Coefficients
| Coefficient sign | Meaning |
|---|---|
| Positive βⱼ | Increases the log-odds (and probability) of the positive class |
| Negative βⱼ | Decreases the log-odds (and probability) of the positive class |
| Magnitude | βⱼ |
After training, we examine log.coef_ in this notebook to see which features most strongly predict survival.
Decision Threshold & Probability Output
By default, scikit-learn classifies an observation as class 1 if P(y=1) >= 0.5, otherwise class 0. This threshold can be adjusted:
- Lower threshold (e.g., 0.3) → more positive predictions (higher recall, lower precision)
- Higher threshold (e.g., 0.7) → fewer positive predictions (higher precision, lower recall)
predict_proba() returns the raw probabilities, which we use later in this notebook for threshold optimization and detailed prediction analysis.
Logistic Regression vs. Linear Regression
Logistic Regression vs. Linear Regression
| Aspect | Linear Regression | Logistic Regression |
|---|---|---|
| Output | Continuous value | Probability (0 to 1) |
| Link function | Identity | Sigmoid (logit) |
| Loss function | Mean Squared Error | Log-loss (cross-entropy) |
| Use case | Predicting quantities | Binary classification |
Strengths & Limitations
Strengths:
- Simple, fast, and highly interpretable
- Outputs well-calibrated probabilities
- Works well as a baseline classifier
- Less prone to overfitting with small datasets (with regularization)
Limitations:
- Assumes a linear decision boundary in log-odds space
- Struggles with complex non-linear relationships
- Sensitive to feature scaling (we apply StandardScaler in this notebook)
- Requires thoughtful handling of missing values and categorical encoding
CODES
# =============================================================================
# IMPORTS AND ENVIRONMENT SETUP
# =============================================================================
# Import core libraries for numerics, data handling, and plotting
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
#import pandas_profiling
%matplotlib inline
!pip install tqdm
# Enable multiple expressions per cell in Jupyter (shows all results, not just last)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = “all”
# =============================================================================
# EXPLORATORY DATA ANALYSIS — Titanic Dataset Overview
# =============================================================================
# Load a fresh copy of the Titanic dataset for initial exploration
titanic_eda = sns.load_dataset(‘titanic’)
# — Dataset shape and basic statistics —
print(“=” * 60)
print(“TITANIC DATASET — EXPLORATORY DATA ANALYSIS”)
print(“=” * 60)
print(f”\nDataset Shape: {titanic_eda.shape[0]} rows x {titanic_eda.shape[1]} columns”)
print(f”Overall Survival Rate: {titanic_eda[‘survived’].mean():.1%}”)
print(“\n— Missing Values —“)
print(titanic_eda.isna().sum())
print(“\n— Survival by Gender —“)
print(titanic_eda.groupby(‘sex’)[‘survived’].mean().round(3))
print(“\n— Survival by Passenger Class —“)
print(titanic_eda.groupby(‘pclass’)[‘survived’].mean().round(3))
# — Chart 1: Overview panel (survival, gender, class, age) —
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
sns.countplot(data=titanic_eda, x=’survived’, ax=axes[0, 0], palette=’Set2′)
axes[0, 0].set_title(‘Survival Count (0 = Died, 1 = Survived)’)
axes[0, 0].set_xlabel(‘Survived’)
sns.countplot(data=titanic_eda, x=’sex’, hue=’survived’, ax=axes[0, 1], palette=’Set1′)
axes[0, 1].set_title(‘Survival by Gender’)
axes[0, 1].legend(title=’Survived’)
sns.countplot(data=titanic_eda, x=’pclass’, hue=’survived’, ax=axes[1, 0], palette=’Set1′)
axes[1, 0].set_title(‘Survival by Passenger Class’)
axes[1, 0].set_xlabel(‘Passenger Class’)
axes[1, 0].legend(title=’Survived’)
sns.histplot(data=titanic_eda, x=’age’, hue=’survived’, kde=True, ax=axes[1, 1], palette=’Set1′)
axes[1, 1].set_title(‘Age Distribution by Survival Status’)
plt.suptitle(‘Titanic EDA — Key Survival Patterns’, fontsize=14, y=1.02)
plt.tight_layout()
plt.show()
# — Chart 2: Fare distribution and missing values —
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
sns.boxplot(data=titanic_eda, x=’survived’, y=’fare’, ax=axes[0], palette=’Pastel1′)
axes[0].set_title(‘Fare Distribution by Survival Status’)
axes[0].set_xlabel(‘Survived (0 = Died, 1 = Survived)’)
missing = titanic_eda.isna().sum()
missing = missing[missing > 0].sort_values(ascending=False)
missing.plot(kind=’bar’, ax=axes[1], color=’coral’, edgecolor=’black’)
axes[1].set_title(‘Missing Values by Column’)
axes[1].set_ylabel(‘Count’)
axes[1].tick_params(axis=’x’, rotation=45)
plt.tight_layout()
plt.show()
# — Chart 3: Correlation heatmap for numeric features —
numeric_cols = titanic_eda.select_dtypes(include=’number’).columns
corr = titanic_eda[numeric_cols].corr()
plt.figure(figsize=(8, 6))
sns.heatmap(corr, annot=True, fmt=’.2f’, cmap=’coolwarm’, center=0, square=True)
plt.title(‘Correlation Heatmap — Numeric Features’)
plt.tight_layout()
plt.show()
# =============================================================================
# LOAD TITANIC DATA
# =============================================================================
# Load the built-in Titanic dataset from seaborn and preview first/last rows
titanic = sns.load_dataset(‘titanic’)
titanic.head()
titanic.tail()
# =============================================================================
# PROGRESS BAR DEMO (tqdm)
# =============================================================================
# Demonstrate tqdm progress bar utility
from tqdm import tqdm
from time import sleep
with tqdm(total=100) as pbar:
for i in range(10):
sleep(0.1)
pbar.update(10)
# =============================================================================
# DROP REDUNDANT / LEAKAGE COLUMNS
# =============================================================================
# Remove columns that duplicate information or would leak the target variable
# Note: ‘deck’ is kept here because it is imputed later in the missing-value section
titanic.drop([‘alive’, ‘class’,’who’, ’embark_town’, ‘alone’, ‘adult_male’]
,axis=1,inplace=True)
# Preview cleaned dataframe
titanic.head()
# Check data types and non-null counts
titanic.info()
# Explore embarked port distribution before handling missing values
titanic[’embarked’].value_counts()
# Find the most frequent embarkation port (used for imputation)
titanic[’embarked’].mode()
# Inspect rows with missing embarked values
titanic.iloc[[61,684],:]
titanic.info()
# Fill missing embarked values with the mode (most common port: Southampton)
titanic[’embarked’] = titanic[’embarked’].fillna(titanic[’embarked’].mode()[0])
titanic.info()
# Summary of remaining missing values across all columns
titanic.isna().sum()
titanic.shape
# =============================================================================
# REMOVE DUPLICATE ROWS
# =============================================================================
# drop duplicates
titanic = titanic.drop_duplicates()
titanic.shape
# =============================================================================
# EXPLORE MISSING AGE VALUES
# =============================================================================
# Isolate passengers with missing age for inspection
titanic_age_missing = titanic[titanic[‘age’].isna()]
titanic_age_missing.head()
titanic_age_missing.shape
# Inspect specific rows of interest
titanic.iloc[[19,26,28],:]
# =============================================================================
# RENAME COLUMNS FOR CLARITY
# =============================================================================
# Standardize column names to descriptive lowercase labels
titanic = titanic.rename(columns = {“sex”:”gender”, “sibsp”:’siblings_spouse’,
“parch”:”parents_child”,”Survived”:’survived’,
“Pclass”:’plcass’,
‘Age’:’age’,’Fare’:’fare’, ‘Embarked’:’embarked’})
titanic.head()
titanic.info()
titanic.columns
titanic[’embarked’].value_counts()
# =============================================================================
# DECK COLUMN — MISSING VALUE TREATMENT
# =============================================================================
# Convert deck to object type and fill missing deck values with ‘Not Assigned’
# (Must run before categorical encoding so deck is not dropped and median fill works)
titanic.deck = titanic.deck.astype(‘object’)
titanic[‘deck’] = titanic[‘deck’].fillna(‘Not Assigned’)
titanic.info()
titanic.deck.isna().sum()
# =============================================================================
# ENCODE CATEGORICAL VARIABLES AS INTEGER CODES
# =============================================================================
# Convert object-type columns to numeric category codes for modeling
for x in titanic.columns:
if titanic[x].dtype == “object”:
titanic[x]=pd.Categorical(titanic[x]).codes
titanic.head()
# =============================================================================
# IMPUTE MISSING AGE WITH MEDIAN
# =============================================================================
# Compute median age and fill missing age values
titanic[‘age’].median()
titanic[‘age’] = titanic[‘age’].fillna(titanic[‘age’].median())
titanic.info()
# Fill any remaining numeric missing values with column medians
titanic= titanic.fillna(titanic.median())
titanic.info()
titanic.iloc[[19,26,28],:]
# =============================================================================
# HANDLE REMAINING EMBARKED MISSING VALUES
# =============================================================================
titanic_embark_missing = titanic[titanic[’embarked’].isna()]
titanic_embark_missing
titanic[’embarked’].mode()
titanic[’embarked’] = titanic[’embarked’].fillna(titanic[’embarked’].mode()[0])
titanic.iloc[[61,684],:]
# Sample and inspect the cleaned dataset
titanic.head(10)
titanic.sample(10)
titanic.tail(10)
titanic.info()
titanic.columns
titanic.info()
# =============================================================================
# DESCRIPTIVE STATISTICS AND OUTLIER ANALYSIS
# =============================================================================
# Summary statistics for all numeric columns
titanic.describe()
# Percentile distribution to identify potential outliers
titanic.quantile((0, 0.01,0.05, 0.5, 0.95,0.99, 1.0))
# Visualize fare distribution before clipping outliers
titanic.fare.hist()
# Clip fare outliers at the 99th percentile upper bound
titanic.fare = np.clip(titanic.fare,0,249.00622 )
titanic.fare.describe()
titanic.fare.hist()
titanic.info()
titanic.describe()
# Clip all numeric columns to their 1st and 99th percentile bounds
for x in titanic.columns:
outlier = titanic[x].quantile([0.01,0.99]).values
titanic[x] = np.clip(titanic[x], outlier[0], outlier[1])
titanic.describe()
# prompt: read a csv file
#df = pd.read_csv(‘filename.csv’)
titanic.describe()
titanic.head()
# Check class balance of the target variable (survived)
titanic.groupby(‘survived’).size()
titanic.shape
# =============================================================================
# PREPARE FEATURES (X) AND TARGET (y)
# =============================================================================
# Import scikit-learn modules for splitting, metrics, and logistic regression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
# Separate features from target variable
x = titanic.drop([‘survived’], axis = 1, inplace=False)
y = titanic[‘survived’]
x.shape
print(‘\n’)
y.shape
titanic.describe()
# =============================================================================
# FEATURE SCALING — MinMaxScaler (demonstration)
# =============================================================================
from sklearn.preprocessing import MinMaxScaler
scld_titanic = MinMaxScaler(feature_range= (0,1))
titanic_transformed = scld_titanic.fit_transform(x)
scld_titanic_df = pd.DataFrame(titanic_transformed, columns = x.columns)
scld_titanic_df.head()
scld_titanic_df.describe()
# =============================================================================
# FEATURE SCALING — StandardScaler (used for modeling)
# =============================================================================
from sklearn.preprocessing import StandardScaler
scld_titanic = StandardScaler()
titanic_transformed = scld_titanic.fit_transform(x)
scld_titanic_df = pd.DataFrame(titanic_transformed, columns = x.columns)
scld_titanic_df.head()
np.round(scld_titanic_df.describe(),2)
# =============================================================================
# TRAIN / TEST SPLIT
# =============================================================================
# Split in Train and Test data
x_train, x_test, y_train, y_test = train_test_split(scld_titanic_df, y, test_size=0.2, random_state=999)
x_train.shape
y_train.shape
x_test.shape
y_test.shape
x_train
y_train
x_test
# Check survival rate (% positive class) in train and test sets
np.round(y_train.sum()/y_train.count()*100,2)
print(‘\n’)
np.round(y_test.sum()/y_test.count()*100,2)
# =============================================================================
# LOGISTIC REGRESSION MODEL
# =============================================================================
# Initialize logistic regression classifier
log = LogisticRegression()
log
# Fit the model on training data
log.fit(x_train, y_train)
# =============================================================================
# PREDICTIONS AND EVALUATION
# =============================================================================
# Generate class predictions on the test set
predicted = log.predict(x_test)
predicted
from sklearn import metrics
print((metrics.classification_report(y_test, predicted)))
# Build confusion matrix comparing actual vs predicted labels
df_confusion = metrics.confusion_matrix(y_test,predicted)
df_confusion
import seaborn as sns
import matplotlib.pyplot as plt
# Visualize confusion matrix as a heatmap
sns.heatmap(df_confusion, cmap = ‘Greens’,xticklabels=[‘Prediction No’,’Prediction Yes’],
yticklabels=[‘Actual No’,’Actual Yes’], annot=True, fmt=’d’)
plt.show();
# Overall accuracy score
metrics.accuracy_score(y_test, predicted)
# =============================================================================
# MODEL INTERPRETATION — COEFFICIENTS
# =============================================================================
# Raw model coefficients (log-odds impact of each feature)
log.coef_
x.columns
# Combine feature names with their coefficients for readability
coeff = pd.concat([pd.DataFrame(x.columns), pd.DataFrame(np.transpose(log.coef_))],axis =1 )
coeff.columns = (“Variable”, ‘Coeff’)
coeff
# =============================================================================
# PROBABILITY PREDICTIONS AND THRESHOLD ANALYSIS
# =============================================================================
# Find out probability of the classes and predicted classes
predicted_prob = log.predict_proba(x_test)
predicted_prob_df = pd.DataFrame(predicted_prob)
predicted_classes_df = pd.DataFrame(predicted)
y_actual_df = pd.DataFrame(y_test.values)
predicted_df = pd.concat([predicted_prob_df, predicted_classes_df, y_actual_df], axis=1)
predicted_df.columns = [‘Prob_0′,’Prob_1′,’Predicted_Class’, ‘Actual_Class’]
predicted_df.sample(20)
predicted_prob
Output Block
Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3)
============================================================
TITANIC DATASET — EXPLORATORY DATA ANALYSIS
============================================================
Dataset Shape: 891 rows x 15 columns
Overall Survival Rate: 38.4%
— Missing Values —
survived 0
pclass 0
sex 0
age 177
sibsp 0
parch 0
fare 0
embarked 2
class 0
who 0
adult_male 0
deck 688
embark_town 2
alive 0
alone 0
dtype: int64
— Survival by Gender —
sex
female 0.742
male 0.189
Name: survived, dtype: float64
— Survival by Passenger Class —
pclass
1 0.630
2 0.473
3 0.242
Name: survived, dtype: float64
/tmp/ipykernel_597/1527970602.py:40: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.countplot(data=titanic_eda, x=’survived’, ax=axes[0, 0], palette=’Set2′)
/tmp/ipykernel_597/1527970602.py:63: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(data=titanic_eda, x=’survived’, y=’fare’, ax=axes[0], palette=’Pastel1′)
100%|██████████| 100/100 [00:01<00:00, 98.40it/s]
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 deck 203 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 57.0+ KB
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 deck 203 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 57.0+ KB
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 891 non-null object
8 deck 203 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 57.0+ KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null object
3 age 678 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null object
8 deck 202 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 56.2+ KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null object
3 age 678 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null object
8 deck 784 non-null object
dtypes: float64(2), int64(4), object(3)
memory usage: 61.2+ KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class ‘pandas.core.frame.DataFrame’>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
precision recall f1-score support
0 0.79 0.83 0.81 90
1 0.76 0.70 0.73 67
accuracy 0.78 157
macro avg 0.77 0.77 0.77 157
weighted avg 0.78 0.78 0.78 157
array([[0.55397344, 0.44602656],
[0.36648402, 0.63351598],
[0.82161992, 0.17838008],
[0.14546149, 0.85453851],
[0.84317581, 0.15682419],
[0.85898305, 0.14101695],
[0.1203916 , 0.8796084 ],
[0.53805585, 0.46194415],
[0.35717302, 0.64282698],
[0.32849211, 0.67150789],
[0.8049969 , 0.1950031 ],
[0.91066252, 0.08933748],
[0.14053402, 0.85946598],
[0.80303192, 0.19696808],
[0.20489829, 0.79510171],
[0.53863242, 0.46136758],
[0.83888713, 0.16111287],
[0.74881101, 0.25118899],
[0.87802515, 0.12197485],
[0.06159685, 0.93840315],
[0.27139185, 0.72860815],
[0.86190473, 0.13809527],
[0.75697559, 0.24302441],
[0.54108302, 0.45891698],
[0.44893086, 0.55106914],
[0.24285506, 0.75714494],
[0.92521486, 0.07478514],
[0.44515852, 0.55484148],
[0.91673444, 0.08326556],
[0.88859559, 0.11140441],
[0.94591075, 0.05408925],
[0.93521218, 0.06478782],
[0.62625257, 0.37374743],
[0.92211925, 0.07788075],
[0.45685666, 0.54314334],
[0.84312216, 0.15687784],
[0.88547452, 0.11452548],
[0.30936126, 0.69063874],
[0.07939955, 0.92060045],
[0.93219874, 0.06780126],
[0.63330031, 0.36669969],
[0.92053872, 0.07946128],
[0.92868217, 0.07131783],
[0.5183927 , 0.4816073 ],
[0.1146845 , 0.8853155 ],
[0.72566823, 0.27433177],
[0.48886081, 0.51113919],
[0.16107575, 0.83892425],
[0.53904267, 0.46095733],
[0.72140052, 0.27859948],
[0.62960379, 0.37039621],
[0.89962517, 0.10037483],
[0.38187136, 0.61812864],
[0.84040645, 0.15959355],
[0.83113253, 0.16886747],
[0.37034959, 0.62965041],
[0.70190266, 0.29809734],
[0.77624547, 0.22375453],
[0.43187485, 0.56812515],
[0.07140916, 0.92859084],
[0.20527775, 0.79472225],
[0.61233815, 0.38766185],
[0.88753928, 0.11246072],
[0.37644464, 0.62355536],
[0.94206494, 0.05793506],
[0.24231454, 0.75768546],
[0.38422862, 0.61577138],
[0.89253394, 0.10746606],
[0.91772881, 0.08227119],
[0.81285549, 0.18714451],
[0.42634743, 0.57365257],
[0.52135864, 0.47864136],
[0.84618193, 0.15381807],
[0.67191945, 0.32808055],
[0.24777467, 0.75222533],
[0.34002452, 0.65997548],
[0.84284584, 0.15715416],
[0.80420694, 0.19579306],
[0.32039026, 0.67960974],
[0.33664048, 0.66335952],
[0.89465304, 0.10534696],
[0.85935537, 0.14064463],
[0.58474389, 0.41525611],
[0.38899587, 0.61100413],
[0.26705897, 0.73294103],
[0.13009479, 0.86990521],
[0.20701663, 0.79298337],
[0.18618385, 0.81381615],
[0.74663678, 0.25336322],
[0.74368753, 0.25631247],
[0.58907781, 0.41092219],
[0.12987609, 0.87012391],
[0.8431743 , 0.1568257 ],
[0.18348496, 0.81651504],
[0.03473669, 0.96526331],
[0.59965075, 0.40034925],
[0.90786111, 0.09213889],
[0.40560793, 0.59439207],
[0.9645318 , 0.0354682 ],
[0.95093358, 0.04906642],
[0.23132857, 0.76867143],
[0.92231606, 0.07768394],
[0.74551094, 0.25448906],
[0.879819 , 0.120181 ],
[0.65771473, 0.34228527],
[0.10796858, 0.89203142],
[0.89633336, 0.10366664],
[0.19389314, 0.80610686],
[0.94162223, 0.05837777],
[0.58125657, 0.41874343],
[0.17418465, 0.82581535],
[0.89577491, 0.10422509],
[0.84518367, 0.15481633],
[0.80321262, 0.19678738],
[0.76732066, 0.23267934],
[0.83516388, 0.16483612],
[0.75066159, 0.24933841],
[0.2825069 , 0.7174931 ],
[0.42468534, 0.57531466],
[0.85903103, 0.14096897],
[0.5367603 , 0.4632397 ],
[0.89319696, 0.10680304],
[0.41541597, 0.58458403],
[0.91095574, 0.08904426],
[0.85926671, 0.14073329],
[0.15205884, 0.84794116],
[0.837398 , 0.162602 ],
[0.82548683, 0.17451317],
[0.10576671, 0.89423329],
[0.91733691, 0.08266309],
[0.05606322, 0.94393678],
[0.85412342, 0.14587658],
[0.93426411, 0.06573589],
[0.46850755, 0.53149245],
[0.16914952, 0.83085048],
[0.12045064, 0.87954936],
[0.88320405, 0.11679595],
[0.20782634, 0.79217366],
[0.4068325 , 0.5931675 ],
[0.41507059, 0.58492941],
[0.61600861, 0.38399139],
[0.35813055, 0.64186945],
[0.53540125, 0.46459875],
[0.60871701, 0.39128299],
[0.84317102, 0.15682898],
[0.24748414, 0.75251586],
[0.78007734, 0.21992266],
[0.91667162, 0.08332838],
[0.15029365, 0.84970635],
[0.04810056, 0.95189944],
[0.89427977, 0.10572023],
[0.56399894, 0.43600106],
[0.90422189, 0.09577811],
[0.9671217 , 0.0328783 ],
[0.93949532, 0.06050468],
[0.0861572 , 0.9138428 ],
[0.46135897, 0.53864103]])
Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3) ============================================================ TITANIC DATASET — EXPLORATORY DATA ANALYSIS ============================================================ Dataset Shape: 891 rows x 15 columns Overall Survival Rate: 38.4% --- Missing Values --- survived 0 pclass 0 sex 0 age 177 sibsp 0 parch 0 fare 0 embarked 2 class 0 who 0 adult_male 0 deck 688 embark_town 2 alive 0 alone 0 dtype: int64 --- Survival by Gender --- sex female 0.742 male 0.189 Name: survived, dtype: float64 --- Survival by Passenger Class --- pclass 1 0.630 2 0.473 3 0.242 Name: survived, dtype: float64
/tmp/ipykernel_597/1527970602.py:40: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.countplot(data=titanic_eda, x='survived', ax=axes[0, 0], palette='Set2')
/tmp/ipykernel_597/1527970602.py:63: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(data=titanic_eda, x='survived', y='fare', ax=axes[0], palette='Pastel1')
100%|██████████| 100/100 [00:01<00:00, 98.40it/s]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 deck 203 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 57.0+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 889 non-null object
8 deck 203 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 57.0+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 891 non-null int64
1 pclass 891 non-null int64
2 sex 891 non-null object
3 age 714 non-null float64
4 sibsp 891 non-null int64
5 parch 891 non-null int64
6 fare 891 non-null float64
7 embarked 891 non-null object
8 deck 203 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 57.0+ KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null object
3 age 678 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null object
8 deck 202 non-null category
dtypes: category(1), float64(2), int64(4), object(2)
memory usage: 56.2+ KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null object
3 age 678 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null object
8 deck 784 non-null object
dtypes: float64(2), int64(4), object(3)
memory usage: 61.2+ KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
<class 'pandas.core.frame.DataFrame'>
Index: 784 entries, 0 to 890
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 survived 784 non-null int64
1 pclass 784 non-null int64
2 gender 784 non-null int8
3 age 784 non-null float64
4 siblings_spouse 784 non-null int64
5 parents_child 784 non-null int64
6 fare 784 non-null float64
7 embarked 784 non-null int8
8 deck 784 non-null int8
dtypes: float64(2), int64(4), int8(3)
memory usage: 45.2 KB
precision recall f1-score support
0 0.79 0.83 0.81 90
1 0.76 0.70 0.73 67
accuracy 0.78 157
macro avg 0.77 0.77 0.77 157
weighted avg 0.78 0.78 0.78 157
array([[0.55397344, 0.44602656], [0.36648402, 0.63351598], [0.82161992, 0.17838008], [0.14546149, 0.85453851], [0.84317581, 0.15682419], [0.85898305, 0.14101695], [0.1203916 , 0.8796084 ], [0.53805585, 0.46194415], [0.35717302, 0.64282698], [0.32849211, 0.67150789], [0.8049969 , 0.1950031 ], [0.91066252, 0.08933748], [0.14053402, 0.85946598], [0.80303192, 0.19696808], [0.20489829, 0.79510171], [0.53863242, 0.46136758], [0.83888713, 0.16111287], [0.74881101, 0.25118899], [0.87802515, 0.12197485], [0.06159685, 0.93840315], [0.27139185, 0.72860815], [0.86190473, 0.13809527], [0.75697559, 0.24302441], [0.54108302, 0.45891698], [0.44893086, 0.55106914], [0.24285506, 0.75714494], [0.92521486, 0.07478514], [0.44515852, 0.55484148], [0.91673444, 0.08326556], [0.88859559, 0.11140441], [0.94591075, 0.05408925], [0.93521218, 0.06478782], [0.62625257, 0.37374743], [0.92211925, 0.07788075], [0.45685666, 0.54314334], [0.84312216, 0.15687784], [0.88547452, 0.11452548], [0.30936126, 0.69063874], [0.07939955, 0.92060045], [0.93219874, 0.06780126], [0.63330031, 0.36669969], [0.92053872, 0.07946128], [0.92868217, 0.07131783], [0.5183927 , 0.4816073 ], [0.1146845 , 0.8853155 ], [0.72566823, 0.27433177], [0.48886081, 0.51113919], [0.16107575, 0.83892425], [0.53904267, 0.46095733], [0.72140052, 0.27859948], [0.62960379, 0.37039621], [0.89962517, 0.10037483], [0.38187136, 0.61812864], [0.84040645, 0.15959355], [0.83113253, 0.16886747], [0.37034959, 0.62965041], [0.70190266, 0.29809734], [0.77624547, 0.22375453], [0.43187485, 0.56812515], [0.07140916, 0.92859084], [0.20527775, 0.79472225], [0.61233815, 0.38766185], [0.88753928, 0.11246072], [0.37644464, 0.62355536], [0.94206494, 0.05793506], [0.24231454, 0.75768546], [0.38422862, 0.61577138], [0.89253394, 0.10746606], [0.91772881, 0.08227119], [0.81285549, 0.18714451], [0.42634743, 0.57365257], [0.52135864, 0.47864136], [0.84618193, 0.15381807], [0.67191945, 0.32808055], [0.24777467, 0.75222533], [0.34002452, 0.65997548], [0.84284584, 0.15715416], [0.80420694, 0.19579306], [0.32039026, 0.67960974], [0.33664048, 0.66335952], [0.89465304, 0.10534696], [0.85935537, 0.14064463], [0.58474389, 0.41525611], [0.38899587, 0.61100413], [0.26705897, 0.73294103], [0.13009479, 0.86990521], [0.20701663, 0.79298337], [0.18618385, 0.81381615], [0.74663678, 0.25336322], [0.74368753, 0.25631247], [0.58907781, 0.41092219], [0.12987609, 0.87012391], [0.8431743 , 0.1568257 ], [0.18348496, 0.81651504], [0.03473669, 0.96526331], [0.59965075, 0.40034925], [0.90786111, 0.09213889], [0.40560793, 0.59439207], [0.9645318 , 0.0354682 ], [0.95093358, 0.04906642], [0.23132857, 0.76867143], [0.92231606, 0.07768394], [0.74551094, 0.25448906], [0.879819 , 0.120181 ], [0.65771473, 0.34228527], [0.10796858, 0.89203142], [0.89633336, 0.10366664], [0.19389314, 0.80610686], [0.94162223, 0.05837777], [0.58125657, 0.41874343], [0.17418465, 0.82581535], [0.89577491, 0.10422509], [0.84518367, 0.15481633], [0.80321262, 0.19678738], [0.76732066, 0.23267934], [0.83516388, 0.16483612], [0.75066159, 0.24933841], [0.2825069 , 0.7174931 ], [0.42468534, 0.57531466], [0.85903103, 0.14096897], [0.5367603 , 0.4632397 ], [0.89319696, 0.10680304], [0.41541597, 0.58458403], [0.91095574, 0.08904426], [0.85926671, 0.14073329], [0.15205884, 0.84794116], [0.837398 , 0.162602 ], [0.82548683, 0.17451317], [0.10576671, 0.89423329], [0.91733691, 0.08266309], [0.05606322, 0.94393678], [0.85412342, 0.14587658], [0.93426411, 0.06573589], [0.46850755, 0.53149245], [0.16914952, 0.83085048], [0.12045064, 0.87954936], [0.88320405, 0.11679595], [0.20782634, 0.79217366], [0.4068325 , 0.5931675 ], [0.41507059, 0.58492941], [0.61600861, 0.38399139], [0.35813055, 0.64186945], [0.53540125, 0.46459875], [0.60871701, 0.39128299], [0.84317102, 0.15682898], [0.24748414, 0.75251586], [0.78007734, 0.21992266], [0.91667162, 0.08332838], [0.15029365, 0.84970635], [0.04810056, 0.95189944], [0.89427977, 0.10572023], [0.56399894, 0.43600106], [0.90422189, 0.09577811], [0.9671217 , 0.0328783 ], [0.93949532, 0.06050468], [0.0861572 , 0.9138428 ], [0.46135897, 0.53864103]])
Intercepts for the Logistic Regression
plt.figure(figsize=(10, 6))sns.barplot(x='Variable', y='Coeff', data=coeff, palette='viridis')plt.title('Logistic Regression Coefficients (with Intercept)')plt.xlabel('Feature Variables')plt.ylabel('Coefficient Value')plt.xticks(rotation=45, ha='right')plt.tight_layout()plt.show()
<Figure size 1000x600 with 0 Axes>
/tmp/ipykernel_597/2760321974.py:2: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(x='Variable', y='Coeff', data=coeff, palette='viridis')
<Axes: xlabel='Variable', ylabel='Coeff'>
Text(0.5, 1.0, 'Logistic Regression Coefficients (with Intercept)')
Text(0.5, 0, 'Feature Variables')
Text(0, 0.5, 'Coefficient Value')
([0, 1, 2, 3, 4, 5, 6, 7, 8], [Text(0, 0, 'pclass'), Text(1, 0, 'gender'), Text(2, 0, 'age'), Text(3, 0, 'siblings_spouse'), Text(4, 0, 'parents_child'), Text(5, 0, 'fare'), Text(6, 0, 'embarked'), Text(7, 0, 'deck'), Text(8, 0, 'Intercept')])
RP’s Blog on AI 
You must be logged in to post a comment.