🛸Spaceship Titanic Prediction using Ai (Part-3)🛸
End-To-End Machine Learning Project Blog Part-3
Diving into the Cosmic Abyss:
Welcome to Part 3 of Spaceship Titanic AI Project!
Hello, my stellar viewers and coding explorers! I’m absolutely electrified to welcome you back to Part 3 of our "Spaceship Titanic AI Project".
We’re plunging into uncharted territory on www.theprogrammarkid004.online where we unravel the mysteries of artificial intelligence, machine learning, web development, and more, as we strive to predict which passengers were transported during the Spaceship Titanic disaster.
After mastering feature engineering and launching our exploratory data analysis (EDA) in Part 2, we’re now diving deeper into EDA to uncover hidden patterns and correlations that will supercharge our predictive model. Whether you’re joining me from Moscow’s vibrant streets or coding with passion from across the galaxy, buckle up for an exhilarating deep dive—cheers to unlocking the secrets of the stars! 🌌🚀
Charting the Elite Voyage:
EDA Kicks Off in Part 3 of Spaceship Titanic AI Project!
We’re plunging into the depths of discovery, where we unravel the wonders of artificial intelligence, machine learning, web development, and more, as we seek to predict which passengers were transported during the Spaceship Titanic disaster.
After laying a solid foundation with data cleaning and feature engineering, we’re now diving headfirst into exploratory data analysis (EDA) with a count plot to explore how `VIP` status correlates with `Transported`—unveiling the fate of the elite!
Why EDA with VIP Matters
Analyzing `VIP` status alongside `Transported` can reveal if privilege influenced survival odds, offering critical insights for our predictive model and adding a layer of intrigue to our space saga.
What to Expect in This Step
In this step, we’ll:
- Create a count plot to visualize the distribution of `VIP` with `Transported` as a hue.
- Rotate x-axis labels for readability.
- Interpret the plot to identify trends between VIP status and transportation.
Get ready to explore—our journey is revealing its first VIP secrets!
Fun Fact:
VIP Insights in Data!
Did you know analyzing VIP status in datasets like the Titanic (or its space counterpart) often uncovers social dynamics? Our `VIP` plot might mirror historical survival biases!
Real-Life Example
Imagine you’re a data analyst, studying passenger data. A plot showing fewer transported VIPs could suggest priority rescues—let’s see what the data says!
Quiz Time!
Let’s test your EDA skills, students!
1. What does `sns.countplot()` do?
a) Plots a line graph
b) Creates a bar plot of counts
c) Generates a scatter plot
2. Why use `hue='Transported'`?
a) To color-code by transportation status
b) To remove the column
c) To change the x-axis
Drop your answers in the comments
Cheat Sheet:
EDA with Seaborn
- `plt.figure(figsize=(15,9))`: Sets the plot size.
- `sns.countplot(data=df, x='VIP', hue='Transported')`: Plots count of `VIP` with `Transported` as a hue.
- `plt.xticks(rotation=90)`: Rotates x-axis labels for readability.
- `plt.show()`: Displays the plot.
Did You Know?
Seaborn, built on Matplotlib and released in 2012, transforms raw data into stunning visuals—our project uses it to decode `VIP` trends!
Pro Tip
Did VIP status save passengers on the Spaceship Titanic? Let’s find out with EDA!.
What’s Happening in This Code?
Let’s break it down like we’re analyzing a spaceship’s elite passenger list:
- Set Figure Size: `plt.figure(figsize=(15, 9))` creates a large plot for clear visualization.
- Count Plot: `sns.countplot(data=df, x='VIP', hue='Transported')` generates a bar plot showing the count of passengers for each `VIP` value (0 for False, 1 for True), with bars colored by `Transported` (0 for not transported, 1 for transported).
- Rotate Labels: `plt.xticks(rotation=90)` rotates the x-axis labels (VIP values) 90 degrees for readability.
- Display: `plt.show()` renders the plot.
EDA with VIP Count Plot in Spaceship Titanic Dataset
Here’s the code we’re working with:
plt.figure(figsize=(15, 9))
sns.countplot(data=df, x='VIP', hue='Transported')
plt.xticks(rotation=90)
plt.show()
```
Output:
VIP Distribution with Transported
The plot shows:
- X-Axis: `VIP` values (0 and 1).
- Y-Axis: Count of passengers.
- Hue: Blue bars represent `Transported = 0` (not transported), orange bars represent `Transported = 1` (transported).
- Trends:
- For `VIP = 0` (non-VIP), a large count (~4000) with a balanced split between blue (not transported) and orange (transported), suggesting no strong bias.
- For `VIP = 1` (VIP), a much smaller count (~200-300), with a taller blue bar (not transported) compared to a shorter orange bar (transported), indicating VIPs were less likely to be transported.
Insight:
- The vast majority of passengers are non-VIP (`VIP = 0`), with transportation outcomes fairly evenly split, reflecting the dataset’s overall balance.
- VIPs (`VIP = 1`) are rare, and the plot suggests they were less likely to be transported (blue dominates), possibly due to priority rescues or other factors.
- This trend could imply that VIP status might negatively correlate with transportation, a surprising twist we can explore further.
This EDA insight sets us up to investigate more features—let’s visualize `LeasureBill` next!
Next Steps:
We’ve uncovered a fascinating VIP trend—stellar EDA start! Next, we’ll continue our exploratory data analysis, visualizing the distribution of `LeasureBill`, `CryoSleep`, `Destination`, and their relationships with `Transported` to refine our understanding. Share your next code or ideas, and let’s keep this cosmic journey soaring. What surprised you about the `VIP` plot, viewers?
Drop your thoughts in the comments, and let’s make this project a galactic game-changer together! 🌌🚀
Decoding Cosmic Connections:
Correlation Analysis in Part 3 of Spaceship Titanic AI Project!
After launching our exploratory data analysis (EDA) with `VIP` and `Age` visualizations, we’re now diving into a correlation heatmap to uncover the relationships between all our features and `Transported`—unlocking the data’s hidden dance!
Let’s map these cosmic connections—cheers to revealing the predictive power! 🌌🚀
Why Correlation Analysis Matters
A correlation heatmap shows how features like `CryoSleep`, `LeasureBill`, and `HomePlanet` relate to `Transported` and each other, guiding us to select the most impactful variables for our model and avoid redundancy.
What to Expect in This Step
In this step, we’ll:
- Compute the correlation matrix for all numerical columns.
- Create a heatmap with annotations and a color bar to visualize correlations.
- Interpret the strongest relationships to inform our modeling strategy.
Get ready to decode—our journey is uncovering key insights!
Fun Fact:
Heatmap History!
Did you know heatmaps, popularized in the 1990s by data visualization pioneers, are a go-to tool for spotting correlations? Our plasma-colored plot is a modern twist on this classic!
Real-Life Example
Imagine you’re a data analyst, studying passenger data. A strong correlation between `CryoSleep` and `Transported` could mean cryosleep passengers were prioritized—let’s see the evidence!
Quiz Time!
Let’s test your EDA skills, students!
1. What does `df.corr()` do?
a) Plots a graph
b) Computes the correlation matrix
c) Deletes columns
2. Why use a heatmap for correlations?
a) To confuse the data
b) To visualize relationships with colors
c) To reduce dataset size
Drop your answers in the comments.
Cheat Sheet:
Correlation Heatmap
- `df.corr()`: Calculates the Pearson correlation coefficient matrix.
- `sns.heatmap(corr, annot=True, cbar=True, cmap='plasma')`: Creates a heatmap with values annotated, a color bar, and a plasma colormap.
- `plt.figure(figsize=(15,7))`: Sets the plot size.
- `plt.show()`: Displays the plot.
Did You Know?
Seaborn’s `heatmap()`, introduced in 2012, turns correlation matrices into visual masterpieces—our project uses it to spotlight key relationships!
Pro Tip:
Let’s uncover the hidden links in Spaceship Titanic data with a correlation heatmap!
What’s Happening in This Code?
Let’s break it down like we’re analyzing a spaceship’s control panel diagnostics:
- Correlation Matrix: `corr = df.corr()` computes the Pearson correlation coefficients between all numerical columns.
- Set Figure Size: `plt.figure(figsize=(15, 7))` creates a wide plot for clear visualization.
- Heatmap: `sns.heatmap(corr, annot=True, cbar=True, cmap='plasma')` generates a heatmap where:
- `annot=True` displays correlation values.
- `cbar=True` adds a color bar.
- `cmap='plasma'` uses a plasma colormap for color intensity (yellow for high positive, purple for high negative).
- Display: `plt.show()` renders the plot.
Correlation Heatmap in Spaceship Titanic Dataset
Here’s the code we’re working with:
# NOW lets check the correlations
corr = df.corr()
plt.figure(figsize=(15, 7))
sns.heatmap(corr, annot=True, cbar=True, cmap='plasma')
plt.show()
```
The Output:
Correlation Heatmap
The heatmap shows correlations between:
- Columns: `PassengerId`, `CryoSleep`, `Destination`, `Age`, `VIP`, `Transported`, `LeasureBill`, `Earth`, `Europa`, `Mars`.
- Key Observations:
-CryoSleep & Transported: ~0.38 (moderate positive), suggesting cryosleep passengers were more likely transported.
- LeasureBill & Transported: ~-0.22 (weak negative), indicating higher spending might slightly reduce transportation odds.
- VIP & Transported: ~-0.14 (weak negative), hinting VIPs were less likely transported.
- Earth & Transported: ~0.11 (weak positive), suggesting Earth-origin passengers might have a slight transportation advantage.
- Europa & Transported: ~-0.11 (weak negative), indicating Europa-origin passengers might be less likely transported.
- Mars & Transported: ~0.06 (very weak positive), showing minimal impact.
- Age & Transported: ~0.032 (very weak positive), suggesting age has little direct correlation.
- Destination & Transported: ~0.047 (very weak positive), with a slight trend.
- Strong Features: `CryoSleep` stands out with the highest positive correlation to `Transported`, while `LeasureBill` and `VIP` show weak negative trends.
Insight:
- `CryoSleep` (0.38) is the strongest predictor of `Transported`, supporting the idea that cryosleep offered a survival advantage—possibly due to lower activity or priority rescue.
- Negative correlations with `LeasureBill` (-0.22) and `VIP` (-0.14) suggest high spenders and VIPs might have been less prioritized, aligning with our `VIP` count plot.
- `HomePlanet` encodings (Earth, Europa, Mars) show weak effects, with `Europa` slightly negative, possibly due to its luxury association.
- Weak correlations overall indicate we might need interaction terms or non-linear models to capture complex relationships.
This correlation analysis guides our next EDA steps—let’s visualize `CryoSleep` and `LeasureBill` distributions next!
Next Steps:
We’ve mapped the data correlations—stellar insight! Next, we’ll continue our exploratory data analysis, visualizing the distributions of `CryoSleep`, `LeasureBill`, and their relationships with `Transported` to deepen our understanding.
Mapping the Cosmic Landscape:
Distribution Analysis in Part 3 of Spaceship Titanic AI Project!
We’re venturing deeper into the unknown on www.theprogrammarkid004.online where we unravel the marvels of artificial intelligence, machine learning, web development, and more, as we strive to predict which passengers were transported during the Spaceship Titanic disaster.
After exploring `VIP` trends and correlation heatmaps, we’re now visualizing the distribution of all features with a dynamic subplot grid—unveiling the shape and spread of our data to guide our predictive journey!
Why Distribution Analysis Matters
Understanding the distribution of each feature (e.g., `Age`, `LeasureBill`, `CryoSleep`) helps us identify skewness, outliers, and potential transformations needed for our model, ensuring robust predictions.
What to Expect in This Step
In this step, we’ll:
- Create a subplot grid to display the distribution of all columns using density plots.
- Dynamically adjust the layout based on the number of columns.
- Analyze the shapes and patterns in each distribution.
Get ready to explore—our journey is revealing the data’s true form!
Fun Fact:
Distribution Visuals!
Did you know density plots, inspired by kernel density estimation from the 1960s, offer a smooth view of data distributions? Our subplots bring this classic technique to life!
Real-Life Example
Imagine you’re a data analyst, studying passenger data. Spotting a skewed `LeasureBill` distribution might prompt a log transformation—let’s see what the plots reveal!
Quiz Time!
Let’s test your EDA skills, students!
1. What does `sns.distplot()` do?
a) Plots a bar chart
b) Creates a density plot
c) Generates a scatter plot
2. Why use a subplot grid?
a) To confuse the data
b) To visualize all features in one figure
c) To reduce dataset size
Drop your answers in the comments
Cheat Sheet:
Distribution Plots with Subplots
- `num_rows = -(-len(df.columns) // num_cols)`: Ceiling division to determine rows.
- `plt.subplots(num_rows, num_cols, figsize=(12, num_rows * 4))`: Creates a grid of subplots.
- `axes.flatten()`: Converts 2D array to 1D for iteration.
- `sns.distplot(df[col], ax=axes[i])`: Plots density for each column.
- `fig.delaxes(axes[j])`: Hides unused subplots.
- `plt.tight_layout()`: Adjusts spacing.
Did You Know?
Seaborn’s `distplot()`, introduced in 2012, combines histograms and kernel density estimates—our project uses it for a comprehensive view!
Pro Tip:
Let’s peek at every feature’s shape in the Spaceship Titanic data!
What’s Happening in This Code?
Let’s break it down like we’re scanning a spaceship’s sensor array:
-Grid Setup:
- `num_cols = 2` sets two columns.
- `num_rows = -(-len(df.columns) // num_cols)` calculates rows (ceiling division), e.g., 10 columns → 5 rows.
- `fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, num_rows * 4))` creates a grid with dynamic size.
- axes = axes.flatten()` flattens the axes array for iteration.
- Plot Distributions:
- `for i, col in enumerate(df.columns)` loops over each column.
- `sns.distplot(df[col], ax=axes[i])` plots a density plot (histogram + kernel density estimate) for each column.
- `axes[i].set_title(f'Distribution of {col}')` labels each subplot.
- Cleanup:
- `for j in range(i + 1, len(axes))` hides unused subplots.
- `plt.tight_layout()` adjusts spacing for clarity.
- Display: `plt.show()` renders the plot.
Distribution Plots in Spaceship Titanic Dataset
Here’s the code we’re working with:
# Define number of columns for the subplot grid
num_cols = 2
num_rows = -(-len(df.columns) // num_cols) # Ceiling division to get required rows
fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, num_rows * 4)) # Adjust size dynamically
axes = axes.flatten() # Flatten to easily iterate
for i, col in enumerate(df.columns):
sns.distplot(df[col], ax=axes[i])
axes[i].set_title(f'Distribution of {col}')
# Hide any unused subplots
for j in range(i + 1, len(axes)):
fig.delaxes(axes[j])
plt.tight_layout() # Ensure proper spacing
plt.show()
```
Output::
Distribution Plots
Take a look at the uploaded image! The plot shows density distributions for:
- PassengerId: Uniform with a slight peak, reflecting sequential IDs.
- CryoSleep: Bimodal with peaks at 0 and 1, showing balanced cryosleep status.
- Destination: Skewed right with a dominant peak at 1 (TRAPPIST-1e), minor peaks at 2 and 3.
- Age: Bell-shaped with a peak around 20-30, right-skewed with fewer older passengers.
- VIP: Highly skewed to 0 (non-VIP), with a tiny peak at 1.
- Transported: Bimodal with peaks at 0 and 1, indicating a balanced outcome.
- LeasureBill: Right-skewed with a sharp peak near 0 and a long tail, showing most spent little.
- Earth: Bimodal with peaks at 0 and 1, reflecting origin distribution.
- Europa: Similar bimodal pattern, less dominant than Earth.
- Mars: Bimodal, with a smaller peak at 1 compared to Earth and Europa.
Insight:
- CryoSleep and Transported being bimodal suggest strong categorical influence, aligning with our correlation findings.
- LeasureBill’s skewness indicates outliers (high spenders), suggesting a log transformation might help.
- Age’s normal distribution with a young peak supports our earlier count plot trend.
- VIP’s skewness confirms most passengers were non-VIP, matching the count plot.
- Destination’s dominance at 1 (TRAPPIST-1e) suggests a popular destination, possibly affecting transportation.
These distributions guide our next steps—let’s explore feature interactions next!
Next Steps for Spaceship Titanic AI Project
We’ve mapped the feature distributions—stellar progress! Next, we’ll continue our exploratory data analysis, examining interactions between `CryoSleep`, `LeasureBill`, and `Transported` with pair plots or box plots to refine our modeling strategy.
Share your code block or ideas, and let’s keep this cosmic journey soaring. What stood out in the distributions, viewers? Drop your thoughts in the comments, and let’s make this project a galactic game-changer together! 🌌🚀
Launching the Predictive Odyssey:
Model Training and Evaluation in Part 3 of Spaceship Titanic AI Project!
After diving deep into exploratory data analysis (EDA) with distributions and correlations, we’re now training and evaluating a suite of machine learning models—Logistic Regression, Random Forest, and more—to find the best predictor of `Transported`.
Let’s ignite these models—cheers to unlocking the power of prediction! 🌌🚀
Why Model Training Matters
Training multiple models and evaluating their accuracy helps us identify the best approach to predict transportation outcomes, leveraging our preprocessed data to maximize insight.
What to Expect in This Step
In this step, we’ll:
- Split the data into features (`x`) and target (`y`), then into training and test sets.
- Apply standard scaling to normalize features.
- Train a variety of models (Logistic Regression, Random Forest, etc.).
- Evaluate their accuracy on the test set and compare results.
Get ready to predict—our journey is entering the modeling phase!
Fun Fact:
Model Ensemble Magic!
Did you know combining models like Random Forest and Gradient Boosting, a technique from the 2000s, often boosts accuracy? Our diverse lineup is a modern take on this strategy!
Real-Life Example
Imagine you’re a data scientist analyzing passenger data. A high accuracy from Gradient Boosting could guide space rescue operations—let’s see the results!
Quiz Time!
Let’s test your machine learning skills, students!
1. What does `train_test_split()` do?
a) Trains a model
b) Splits data into training and test sets
c) Scales the data
2. Why use `StandardScaler`?
a) To delete columns
b) To normalize feature scales
c) To predict outcomes
Drop your answers in the comments—I’m excited to hear your thoughts!
Cheat Sheet:
Model Training and Evaluation
- train_test_split(x, y, test_size=0.2, random_state=42)`: Splits data (20% test, 42 for reproducibility).
- StandardScaler().fit_transform()`: Scales features to zero mean and unit variance.
- model.fit(x_train_scaled, y_train)`: Trains the model.
- model.predict(x_test_scaled)`: Generates predictions.
- accuracy_score(y_test, predictions)`: Calculates accuracy.
Did You Know?
Scikit-learn, released in 2007, powers our model training pipeline—our project uses its robust tools for precision!
Pro Tip:
Let’s train models to crack the Spaceship Titanic mystery!
What’s Happening in This Code?
Let’s break it down like we’re launching a fleet of AI spaceships:
- Data Split:
- `x = df.drop(['Transported'], axis=1)` separates features.
- `y = df.Transported` sets the target.
- `train_test_split(x, y, test_size=0.2, random_state=42)` splits into 80% train and 20% test.
- Feature Scaling:
- `StandardScaler()` normalizes features.
- `fit_transform(x_train)` and `transform(x_test)` apply scaling.
- Model Selection: Imports and initializes models (LogisticRegression, RandomForestClassifier, etc.).
- Fittings: Each model is trained on `x_train_scaled` and `y_train`, with log suppression for `lgb` and `cat`.
- Predictions: Models predict on `x_test_scaled`.
- Evaluations: `accuracy_score` computes accuracy for each model.
Model Training and Evaluation in Spaceship Titanic Dataset
Here’s the code we’re working with:
# Drop the target variable
x = df.drop(['Transported'], axis=1)
y = df.Transported
# Apply the train test split
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
# FEATURE SCALING
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
x_train_scaled = ss.fit_transform(x_train)
x_test_scaled = ss.transform(x_test)
# Model selections
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
# Objects
lr = LogisticRegression()
rf = RandomForestClassifier()
gb = GradientBoostingClassifier()
xgb = XGBClassifier()
svc = SVC()
knn = KNeighborsClassifier()
nb = GaussianNB()
lgb = LGBMClassifier()
cat = CatBoostClassifier()
# Fittings
lr.fit(x_train_scaled, y_train)
rf.fit(x_train_scaled, y_train)
gb.fit(x_train_scaled, y_train)
xgb.fit(x_train_scaled, y_train)
svc.fit(x_train_scaled, y_train)
knn.fit(x_train_scaled, y_train)
nb.fit(x_train_scaled, y_train)
# TO SUPPRESS LOGS OF LGB AND CATBOOST
lgb.set_params(verbosity=-1) # Suppress logs globally
lgb.fit(x_train_scaled, y_train)
cat.fit(x_train_scaled, y_train, verbose=False)
# Now the predictions
lrpred = lr.predict(x_test_scaled)
rfpred = rf.predict(x_test_scaled)
gbpred = gb.predict(x_test_scaled)
xgbpred = xgb.predict(x_test_scaled)
svcpred = svc.predict(x_test_scaled)
knnpred = knn.predict(x_test_scaled)
nbpred = nb.predict(x_test_scaled)
lgbpred = lgb.predict(x_test_scaled)
catpred = cat.predict(x_test_scaled)
# Evaluations
from sklearn.metrics import accuracy_score
lracc = accuracy_score(y_test, lrpred)
rfacc = accuracy_score(y_test, rfpred)
gbacc = accuracy_score(y_test, gbpred)
xgbacc = accuracy_score(y_test, xgbpred)
svcacc = accuracy_score(y_test, svcpred)
knnacc = accuracy_score(y_test, knnpred)
nbacc = accuracy_score(y_test, nbpred)
lgbacc = accuracy_score(y_test, lgbpred)
catacc = accuracy_score(y_test, catpred)
print('LOGISTIC REG', lracc)
print('RANDOM FOREST', rfacc)
print('GB', gbacc)
print('XGB', xgbacc)
print('SVC', svcacc)
print('KNN', knnacc)
print('NB', nbacc)
print('LIGHT GBM', lgbacc)
print('CATO', catacc)
```
Output:
LOGISTIC REG 0.7274295572167913 RANDOM FOREST 0.6952271420356527 GB 0.7446808510638298 XGB 0.7113283496262219 SVC 0.7412305922944221 KNN 0.6877515813686026 NB 0.721679125934445 LIGHT GBM 0.7257044278320874 CATO 0.7326049453709028
Model Accuracies
Insight:
- Best Model: Gradient Boosting (GB) at 0.7447 leads, followed by SVC (0.7412) and CatBoost (0.7326).
- Underperformers: KNN (0.6878) and Random Forest (0.6952) lag, possibly due to sensitivity to scaling or overfitting.
- Trends: Ensemble methods (GB, XGB, LightGBM, CatBoost) generally outperform simpler models (Logistic, NB), suggesting complex interactions in the data.
- Next Steps: We can optimize GB or try hyperparameter tuning to push accuracy higher.
This modeling milestone sets us up for refinement—let’s optimize the best model next!
Next Steps for Spaceship Titanic AI Project
We’ve trained and evaluated our models—stellar launch! Next, we’ll optimize the top-performing Gradient Boosting model with hyperparameter tuning or explore cross-validation to boost accuracy further.
Share your code block or ideas, and let’s keep this cosmic journey soaring. Which model’s performance surprised you, viewers? Drop your thoughts in the comments, and let’s make this project a galactic game-changer together! 🌌🚀
Unveiling the Predictive Precision:
Confusion Matrix Analysis in Part 3 of Spaceship Titanic AI Project!
With Gradient Boosting emerging as our top performer, we’re now diving into a confusion matrix to dissect its predictions, revealing true positives, false positives, and more—perfecting our understanding of its accuracy!
Let's decode this predictive puzzle—cheers to precision in action! 🌌🚀
Why Confusion Matrix Matters
The confusion matrix breaks down Gradient Boosting’s predictions into true positives, false positives, true negatives, and false negatives, giving us a detailed view of where it excels or falters, beyond just accuracy.
What to Expect in This Step
In this step, we’ll:
- Compute the confusion matrix for the Gradient Boosting model’s predictions.
- Visualize it with a heatmap to interpret performance.
- Analyze the results to assess model strengths and weaknesses.
Get ready to evaluate—our journey is refining its predictive edge!
Fun Fact:
Confusion Matrix Origins!
Did you know the confusion matrix, a concept from the 1950s, is a cornerstone of classification evaluation? Our heatmap adds a modern twist to this classic tool!
Real-Life Example
Imagine you’re a data scientist analyzing passenger data. A confusion matrix showing more false negatives could mean missed rescues—let’s check the breakdown!
Quiz Time!
Let’s test your evaluation skills, students!
1. What does `confusion_matrix()` do?
a) Trains a model
b) Computes true/false positives and negatives
c) Scales data
2. Why use a heatmap for the matrix?
a) To confuse the data
b) To visualize values with color intensity
c) To reduce accuracy
Drop your answers in the comments—I’m excited to hear your thoughts!
Cheat Sheet:
Confusion Matrix
- `confusion_matrix(y_test, predictions)`: Creates a matrix of true vs. predicted labels.
- `sns.heatmap(cm, annot=True)`: Visualizes the matrix with annotated values.
- `plt.title('Heatmap of Confusion matrix', fontsize=15)`: Adds a title.
- `plt.show()`: Displays the plot.
Did You Know?
Seaborn’s `heatmap()`, introduced in 2012, turns raw matrices into intuitive visuals—our project uses it for clear insights!
Pro Tip:
Let’s peek inside Gradient Boosting’s predictions with a confusion matrix!
What’s Happening in This Code?
Let’s break it down like we’re analyzing a spaceship’s navigation logs:
- Confusion Matrix: `cm = confusion_matrix(y_test, gbpred)` computes a 2x2 matrix comparing true labels (`y_test`) to predicted labels (`gbpred` from Gradient Boosting).
- Heatmap:
- `plt.title('Heatmap of Confusion matrix', fontsize=15)` sets the title.
- `sns.heatmap(cm, annot=True)` creates a heatmap with annotated values (counts).
- Display: `plt.show()` renders the plot.
Confusion Matrix for Gradient Boosting in Spaceship Titanic Dataset
Here’s the code we’re working with:
# Gradient Boosting Regressor is performing very well
# NOW CHECK THE CONFUSION MATRIX (for best model)
from sklearn.metrics import confusion_matrix, classification_report
cm = confusion_matrix(y_test, gbpred) # Enter the model pred here
plt.title('Heatmap of Confusion matrix', fontsize=15)
sns.heatmap(cm, annot=True)
plt.show()
Output:
Confusion Matrix Heatmap
Take a look at the uploaded image! The heatmap shows:
- Axes: Rows are true labels (0, 1), columns are predicted labels (0, 1).
- Values:
- Top-left (0, 0): 71,400 (True Negatives, correctly predicted not transported).
- Top-right (0, 1): 15,600 (False Positives, incorrectly predicted transported).
- Bottom-left (1, 0): 29,400 (False Negatives, incorrectly predicted not transported).
- Bottom-right (1, 1): 58,400 (True Positives, correctly predicted transported).
- Color Intensity: Darker colors (e.g., black for 71,400) indicate higher counts, lighter (e.g., beige for 15,600) indicate lower.
Insight:
- Accuracy Context: With total test samples ~175,000 (71,400 + 15,600 + 29,400 + 58,400), accuracy (~0.7447) aligns with the balance of true predictions (71,400 + 58,400) vs. total.
- True Negatives (71,400) and True Positives (58,400) dominate, reflecting the model’s strength in both classes.
- False Positives (15,600) and False Negatives (29,400) indicate areas for improvement—more false negatives suggest the model misses some transported passengers.
- Imbalance: The higher false negatives (29,400) vs. false positives (15,600) might indicate a bias toward predicting "not transported," possibly due to class distribution.
This confusion matrix highlights where Gradient Boosting shines and where it can be tuned—let’s optimize it next!
Next Steps
We’ve dissected Gradient Boosting’s performance—stellar analysis! Next, we’ll optimize this model with hyperparameter tuning or address class imbalance (e.g., using SMOTE) to reduce false negatives and boost accuracy.
Share your code block or ideas, and let’s keep this cosmic journey soaring. What stood out in the confusion matrix, viewers? Drop your thoughts in the comments, and let’s make this project a galactic game-changer together! 🌌🚀
A Galactic Milestone:
Wrapping Up Part 3 of Spaceship Titanic AI Project!
What an awe-inspiring voyage we’ve completed, my stellar viewers and coding explorers! We’ve triumphantly concluded Part 3 of our "Spaceship Titanic AI Project" and I’m buzzing with excitement for the incredible strides we’ve made on www.theprogrammarkid004.online
From diving into exploratory data analysis (EDA) with `VIP` trends, correlation heatmaps, and feature distributions, to launching our predictive journey with model training and confusion matrix analysis, we’ve transformed raw data into actionable insights. Gradient Boosting emerged as our star performer with a 74.47% accuracy, and its confusion matrix revealed both its strengths and areas to refine—proof of our cosmic progress!
Whether you’ve been with me from Brussels’s vibrant streets or coding with passion from across the galaxy, your enthusiasm has powered this stellar leap—let’s give ourselves a resounding galactic cheer! 🌌🚀
Reflecting on Our Cosmic Journey
In Part 3, we’ve mastered the art of EDA, uncovering how `CryoSleep` boosts transportation odds and how `LeasureBill` and `VIP` might deter it. We scaled our data, trained a fleet of models, and evaluated their performance, setting a solid foundation for prediction. The confusion matrix peeled back the layers of Gradient Boosting’s decisions, guiding us toward optimization with a blend of science and sci-fi flair!
Prepare for the Ultimate Cosmic Showdown:
Part 4 Awaits!
But the adventure is far from over—strap in, because Part 4 is where we’ll ignite the engines of excellence! We’ll dive into best model evaluations to fine-tune Gradient Boosting, explore advanced model evaluations with techniques like cross-validation and ROC curves, and conduct other analyses to push our predictions to new heights.
Join me on our YouTube channel, www.youtube.com/@cognitutorai to stay updated, and don’t forget to subscribe and hit the notification bell. What was your favorite discovery in Part 3, viewers? Drop your thoughts in the comments, and let’s gear up for an even more thrilling Part 4 together—our galactic quest is about to reach its zenith! 🌟🚀