🛸Spaceship Titanic Prediction using Ai (Part-1)🛸
End-To-End Machine Learning Project Blog Part-1
Embarking on a Cosmic Challenge:
Welcome to the Spaceship Titanic AI Project!
Hello, my stellar viewers and coding enthusiasts! I’m beyond thrilled to welcome you to a brand-new adventure on our website, www.theprogrammarkid004.online, where we dive deep into the realms of artificial intelligence, machine learning, web development, and more.
Today we’re launching the "Spaceship Titanic AI Project", a cosmic journey that blends data science with sci-fi intrigue! Inspired by the infamous Titanic but set in the far reaches of space, this project challenges us to predict which passengers aboard the ill-fated Spaceship Titanic were transported to safety during its mysterious disaster. Whether you’re joining me from New York’s vibrant streets or coding with passion from across the galaxy, let’s buckle up and harness the power of AI to solve this interstellar mystery
Cheers to a thrilling new blog series! 🌌🚀
Launching the Mission:
Data Exploration for Spaceship Titanic AI Project!
We’re kicking off this exciting new journey on www.theprogrammarkid004.online where we dive into artificial intelligence, machine learning, web development, and more. Today, we’re boarding the Spaceship Titanic to predict which passengers were transported to safety during its mysterious cosmic disaster. This code block loads and explores the dataset, giving us our first glimpse into features like `HomePlanet`, `CryoSleep`, `Cabin`, and `Transported`.
Whether you’re joining me from your home or coding with passion from across the galaxy, let’s ignite our curiosity and set sail into this interstellar challenge—cheers to an epic coding adventure! 🌌🚀
Why Data Exploration Matters
Understanding the dataset
passenger IDs, origins, amenities, and survival outcomes—lays the foundation for building a predictive model. For our cosmic mission, this step helps us identify key factors that influenced transportation during the disaster.
What to Expect in This Step
In this step, we’ll:
- Import essential libraries for data manipulation and visualization.
- Load the Spaceship Titanic training dataset from Kaggle.
- Display the first few rows to get a feel for the data.
Get ready to explore the stars—our journey has officially begun!
Fun Fact:
Spaceship Titanic Dataset!
Did you know the Spaceship Titanic dataset, inspired by the Kaggle competition, mimics the Titanic challenge but adds a sci-fi twist with features like `CryoSleep` and `Destination`? It’s a perfect playground for AI innovation!
Real-Life Example
Imagine you’re a data analyst, studying passenger data. Spotting patterns in `CryoSleep` and `RoomService` could reveal why some were transported, guiding our model’s focus!
Quiz Time!
Let’s test your data skills, students!
1. What does `df.head()` do?
a) Deletes the dataset
b) Shows the first 5 rows of the dataframe
c) Trains a model
2. Why use `warnings.filterwarnings('ignore')`?
a) To stop all warnings
b) To fix data errors
c) To speed up the code
Drop your answers in the comments
Cheat Sheet:
Data Loading
- `import pandas as pd`: Loads the pandas library for dataframes.
- `pd.read_csv(path)`: Reads a CSV file into a dataframe.
- `df.head()`: Displays the first 5 rows.
Did You Know?
Pandas, created by Wes McKinney in 2008, revolutionized data analysis—our project leverages it to navigate the Spaceship Titanic data!
Pro Tip
Ready to solve a cosmic mystery? Let’s explore the Spaceship Titanic dataset!
Loading and Exploring the Spaceship Titanic Dataset
What’s Happening in This Code?
Let’s break it down like we’re inspecting a spaceship manifest:
- Imports:
- `import pandas as pd` and `import numpy as np` for data manipulation.
- `import matplotlib.pyplot as plt` and `import seaborn as sns` for visualization.
- `import warnings` to manage warning messages.
- Warning Suppression: `warnings.filterwarnings('ignore')` silences non-critical warnings for a cleaner output.
- Data Loading: `df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')` loads the training dataset from the Kaggle path.
- Preview: `df.head()` displays the first 5 rows to explore the data structure.
Here’s the code we’re working with:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.head()
The Output:
First Glance at the Dataset
The dataset includes the following columns:
- PassengerId: Unique identifier (e.g., 0001_01).
- HomePlanet: Origin planet (e.g., Europa, Earth).
- CryoSleep: Boolean indicating if the passenger was in cryosleep (e.g., False).
- Cabin: Cabin number with deck/side info (e.g., B/0/P, F/0/S).
- Destination: Travel destination (e.g., TRAPPIST-1e).
- Age: Passenger age (e.g., 39.0, 24.0).
- VIP: VIP status (e.g., False).
- RoomService, FoodCourt, ShoppingMall, Spa, VRDeck: Spending amounts (e.g., 0.0, 109.0).
- Name: Passenger name (e.g., Maham Ofracculy).
- Transported: Target variable (e.g., False, True), indicating if transported to safety.
Insight: The dataset has 14 columns with a mix of categorical (e.g., `HomePlanet`, `CryoSleep`), numerical (e.g., `Age`, `RoomService`), and text (e.g., `Cabin`, `Name`) features. Missing values (e.g., `NaN` in `ShoppingMall` for row 0) suggest we’ll need data cleaning. `Transported` is our binary target, with a balanced mix of True and False in the sample. Features like `CryoSleep` and spending categories might be key predictors—let’s dive into cleaning and analysis next!
Next Steps for Spaceship Titanic AI Project
We’ve launched our exploration—stellar start! Next, we’ll clean the dataset, handle missing values, and perform exploratory data analysis to uncover patterns.
So let’s keep this cosmic journey soaring. What stood out to you in the data, viewers? Drop your thoughts in the comments! 🌌🚀
Unraveling the Data Universe: Statistical Insights for Spaceship Titanic AI Project!
We’re cruising deeper into this cosmic journey exploring artificial intelligence, machine learning, and more. After loading our Spaceship Titanic dataset, we’re now diving into its statistical summary with `df.describe()` to uncover the distribution of numerical features like `Age`, `RoomService`, and `FoodCourt`.
Let’s analyze these interstellar passenger stats, cheers to decoding the data! 🌌🚀
Why Statistical Summary Matters
The `describe()` output reveals the range, mean, and spread of numerical features, helping us spot outliers, missing values, and patterns that could influence whether passengers were transported. This is our first step toward building a predictive model for the Spaceship Titanic disaster.
What to Expect in This Step
In this step, we’ll:
- Generate a statistical summary of numerical columns in the dataset.
- Analyze key metrics like mean, median, and maximum values.
- Identify potential data cleaning needs based on the results.
Get ready to navigate the data cosmos—our exploration is heating up!
Fun Fact:
Data Summaries in AI!
Did you know `describe()`, introduced with pandas in 2008, is a go-to tool for quick data insights? It’s our launchpad for understanding the Spaceship Titanic passengers!
Real-Life Example
Imagine you’re a data scientist analyzing passenger data. Noticing high `RoomService` max values (e.g., 14,327) could hint at luxury spending patterns affecting transportation chances!
Quiz Time!
Let’s test your data skills, students!
1. What does `df.describe()` show?
a) All columns
b) Statistical summary of numerical columns
c) Only categorical data
2. What does the 75% percentile indicate?
a) Minimum value
b) Value below which 75% of data falls
c) Maximum value
Drop your answers in the comments.
Cheat Sheet:
Statistical Summary
- `df.describe()`: Provides count, mean, std, min, 25%, 50%, 75%, and max for numerical columns.
- Count: Number of non-null values.
- Mean/Std: Average and standard deviation.
- Percentiles: 25%, 50% (median), 75% show data distribution.
Did You Know?
The median (50%) is robust to outliers—our high `RoomService` max (14,327) suggests we’ll need to handle extreme values in the Spaceship Titanic data!
Pro Tip
What secrets hide in the Spaceship Titanic data? Let’s uncover stats with `describe()`!
What’s Happening in This Code?
Let’s break it down like we’re analyzing a spaceship crew report:
- Command: `df.describe()` generates a statistical summary for all numerical columns in the dataframe.
- Output: Displays key metrics (count, mean, std, min, 25%, 50%, 75%, max) for each numerical feature.
Statistical Summary of Spaceship Titanic Dataset
Here’s the code we’re working with:
df.describe()
The Output:
Statistical Summary
Take a look at the uploaded image! The summary covers the following numerical columns:
- Age:
- Count: 8514 (out of ~8693 total rows, indicating ~179 missing values).
- Mean: 28.827930, Median (50%): 27.0, Std: 14.489021.
- Min: 0.0, Max: 79.0, 25%: 19.0, 75%: 38.0.
- RoomService:
- Count: 8512 (similar missingness).
- Mean: 224.687617, Median: 0.0, Std: 666.717663.
- Min: 0.0, Max: 14327.0, 25%: 0.0, 75%: 47.0.
- FoodCourt:
- Count: 8510.
- Mean: 458.077120, Median: 0.0, Std: 1611.489240.
- Min: 0.0, Max: 29813.0, 25%: 0.0, 75%: 76.0.
- ShoppingMall:
- Count: 8485.
- Mean: 173.729169, Median: 0.0, Std: 604.696458.
- Min: 0.0, Max: 23492.0, 25%: 0.0, 75%: 27.0.
- Spa:
- Count: 8510.
- Mean: 311.138778, Median: 0.0, Std: 1136.70551.
- Min: 0.0, Max: 22408.0, 25%: 0.0, 75%: 59.0.
- VRDeck:
- Count: 8505.
- Mean: 304.854251, Median: 0.0, Std: 1145.717192.
- Min: 0.0, Max: 24133.0, 25%: 0.0, 75%: 48.0.
Insight:
- Missing Values: Counts vary (e.g., 8485 for `ShoppingMall` vs. 8514 for `Age`), indicating missing data (~2-3% per column) we’ll need to handle.
- Skewness: Medians at 0 for spending columns (e.g., `RoomService`, `FoodCourt`) with high maxes (e.g., 14,327, 29,813) suggest right-skewed distributions—many passengers spent little, but some spent extravagantly.
- Age Distribution: Ranges from 0 to 79, with a mean of ~28.8, indicating a young-to-middle-aged passenger base.
- Outliers: High max values in spending categories (e.g., `FoodCourt` at 29,813) suggest outliers we might cap or transform.
This summary sets the stage for data cleaning—let’s address missing values and outliers next to prepare for modeling!
Next Steps for Spaceship Titanic AI Project
We’ve mapped the data terrain—stellar insights! Next, we’ll clean the dataset, handle missing values, and visualize distributions to uncover patterns for our prediction model.
Scanning for Data Gaps: Missing Values in Spaceship Titanic AI Project!
We’re making stellar progress on diving deeper into our cosmic challenge to predict which passengers were transported during the Spaceship Titanic disaster. After our statistical summary revealed hints of missing data, we’re now using `df.isnull().sum()` to pinpoint exactly where those gaps are in our dataset.
Why Missing Values Matter
Missing data in features like `CryoSleep` or `RoomService` can skew our model’s predictions for who was transported. Identifying these gaps helps us decide how to handle them—whether by imputation or removal—ensuring our model is robust and accurate.
What to Expect in This Step
In this step, we’ll:
- Check for missing values in each column using `df.isnull().sum()`.
- Analyze the extent of missingness across the dataset.
- Plan our data cleaning strategy based on the results.
Get ready to patch the holes in our data spaceship—our journey is getting smoother!
Fun Fact:
Missing Data Challenges!
Did you know missing data is a common hurdle in machine learning, often affecting up to 10-20% of real-world datasets? Our Spaceship Titanic data gives us a perfect chance to tackle this head-on!
Real-Life Example
Imagine you’re a data engineer preparing passenger records. Finding 201 missing `CryoSleep` entries prompts you to impute them, ensuring accurate predictions for transportation outcomes!
Quiz Time!
Let’s test your data cleaning skills, students!
1. What does `df.isnull().sum()` do?
a) Deletes missing values
b) Counts missing values per column
c) Fills missing values
2. Why address missing values?
a) To increase dataset size
b) To prevent model errors and bias
c) To skip preprocessing
Drop your answers in the comments
Cheat Sheet:
Missing Values Check
- `df.isnull()`: Returns a boolean dataframe where `True` indicates missing values.
- `df.isnull().sum()`: Sums `True` values per column to count missing entries.
- Tip: A high count (e.g., >10%) might require advanced imputation or feature removal.
Did You Know?
Pandas’ `isnull()` method, part of its core since 2008, makes spotting missing data a breeze—our project leverages it to ensure data quality!
Pro Tip
Are there holes in our Spaceship Titanic data? Let’s find out with `isnull().sum()`!
What’s Happening in This Code?
Let’s break it down like we’re scanning a spaceship for breaches:
- Command: `df.isnull().sum()` checks for missing values (`NaN`) in each column and sums them up.
- Output: Displays the count of missing values for each feature.
Checking for Missing Values in Spaceship Titanic Dataset
Here’s the code we’re working with:
df.isnull().sum()
The Output:
Missing Values Count
The output shows the following:
- PassengerId: 0 missing (unique identifier, as expected).
- HomePlanet: 201 missing.
- CryoSleep: 217 missing.
- Cabin: 199 missing.
- Destination: 182 missing.
- Age: 179 missing.
- VIP: 203 missing.
- RoomService: 181 missing.
- FoodCourt: 183 missing.
- ShoppingMall: 208 missing.
- Spa: 183 missing.
- VRDeck: 188 missing.
- Name: 200 missing.
- Transported: 0 missing (target variable, critical for training).
Insight:
- Total Rows: ~8693 (from earlier context), so missing values range from 179 to 217 per column—about 2-2.5% missingness, which is manageable.
- Patterns: Missingness is spread across most columns, with `CryoSleep` (217) and `ShoppingMall` (208) having the highest counts. No missing values in `PassengerId` or `Transported`, which is ideal.
- Strategy: For numerical columns (`Age`, `RoomService`, etc.), we can impute with median or mean (e.g., `Age` median ~27). For categorical columns (`HomePlanet`, `CryoSleep`, `VIP`), mode imputation or a “missing” category might work. `Cabin` (199 missing) is complex—its deck/side info may require custom handling. `Name` (200 missing) might be dropped if not predictive.
This analysis guides our cleaning process—let’s fill these gaps next to ensure our model can navigate smoothly!
Next Steps
We’ve identified the data gaps—great detective work! Next, we’ll clean the dataset by handling these missing values, possibly extracting features from `Cabin`, and preparing for deeper analysis.
Refining Our Cosmic Crew: Cleaning PassengerId in Spaceship Titanic AI Project!
We’re making fantastic progress and now diving deeper into our mission to predict which passengers were transported during the Spaceship Titanic disaster.
After spotting missing values in our dataset, we’re now cleaning the `PassengerId` column by removing underscores and converting it to integers, preparing it for potential feature engineering.
Why Clean PassengerId?
The `PassengerId` (e.g., 0001_01) contains a group identifier and individual number, separated by an underscore. By removing the underscore and converting to an integer, we can simplify it for analysis or extract group-based features later, enhancing our prediction of who was transported.
What to Expect in This Step
In this step, we’ll:
- Remove the underscore from `PassengerId` (e.g., 0001_01 → 000101).
- Convert the cleaned `PassengerId` to an integer type.
- Preview the updated dataframe with `df.head()`.
Get ready to streamline our data—our journey is getting more refined!
Fun Fact:
Feature Engineering Basics!
Did you know cleaning identifiers like `PassengerId` often unlocks hidden patterns? In the Spaceship Titanic dataset, the group number might correlate with transportation outcomes—let’s explore this further!
Real-Life Example
Imagine you’re a data scientist, analyzing passenger logs. Converting `PassengerId` to a single integer (e.g., 000101) lets you group passengers by family or team, revealing if groups were transported together!
Quiz Time!
Let’s test your data cleaning skills, students!
1. What does `str.replace('_','')` do?
a) Adds underscores
b) Removes underscores
c) Changes data type
2. Why use `astype(int)`?
a) To make the column categorical
b) To convert the column to integers
c) To delete the column
Drop your answers in the comments
Cheat Sheet: Data Cleaning
- `str.replace('_','')`: Removes underscores from strings.
- `astype(int)`: Converts a column to integer type.
- Tip: Always check for unexpected characters before type conversion to avoid errors.
Did You Know?
Pandas’ `str` accessor, widely used since 2015, makes string operations like `replace()` a breeze—our project uses it to clean `PassengerId` effortlessly!
Pro Tip:
Let’s clean up our Spaceship Titanic passenger IDs for better analysis!
What’s Happening in This Code?
Let’s break it down like we’re updating a spaceship roster:
- Remove Underscores: `df.PassengerId.str.replace('_','')` removes the underscore from each `PassengerId` (e.g., 0001_01 → 000101).
- Convert to Integer: `df.PassengerId.astype(int)` converts the cleaned string to an integer (e.g., "000101" → 101).
- Preview: `df.head()` displays the first 5 rows to confirm the changes.
Cleaning PassengerId in Spaceship Titanic Dataset
Here’s the code we’re working with:
df.PassengerId = df.PassengerId.str.replace('_','')
df.PassengerId = df.PassengerId.astype(int)
df.head()
The Output:
Updated Dataset Preview
The updated dataframe shows:
- PassengerId: Now integers without underscores:
- Row 0: 101 (was 0001_01).
- Row 1: 201 (was 0002_01).
- Row 2: 301 (was 0003_01).
- Row 3: 302 (was 0003_02).
- Row 4: 401 (was 0004_01).
- Other columns (`HomePlanet`, `CryoSleep`, `Cabin`, etc.) remain unchanged.
Insight:
- The `PassengerId` is now a clean integer, simplifying future analysis. For example, rows 2 and 3 (301, 302) indicate two passengers from the same group (group 0003), which might be predictive if groups were transported together.
- The leading zeros are dropped during the integer conversion (e.g., 000101 → 101), but this doesn’t affect uniqueness since the group and individual numbers are preserved in the sequence.
- We can now extract group numbers (e.g., 101 → group 1) or analyze group sizes to enhance our model—let’s explore this idea in the next step!
Next Steps
We’ve cleaned `PassengerId`—great progress! Next, we’ll extract features like group numbers from `PassengerId`, handle missing values, and continue our data preparation.
Sealing the Data Leaks: Handling Missing Values in Spaceship Titanic AI Project!
We’re making incredible strides on www.theprogrammarkid004.online advancing our mission to predict which passengers were transported during the Spaceship Titanic disaster.
After cleaning `PassengerId`, we’re now tackling missing values by dropping the `Name` column and filling gaps with mean and mode values for numerical and categorical columns, respectively.
Why Handle Missing Values?
Missing data (e.g., 217 in `CryoSleep`) can disrupt model training, leading to biased predictions. By filling numerical columns with means and categorical ones with modes, we ensure a complete dataset for accurate transportation predictions.
What to Expect in This Step
In this step, we’ll:
- Drop the `Name` column, as it’s unlikely to predict transportation.
- Fill missing values in numerical columns (`Age`, `RoomService`, etc.) with their means.
- Fill missing values in categorical columns (`HomePlanet`, `CryoSleep`, etc.) with their modes.
- Verify no missing values remain with `df.isnull().sum()`.
Get ready to patch our dataset—our journey is getting more robust!
Fun Fact:
Imputation Strategies!
Did you know mean/mode imputation, a staple since the early days of data science, is a quick fix for small missingness (<5%)? Our Spaceship Titanic data, with ~2-2.5% missing, is a perfect candidate!
Real-Life Example
Imagine you’re a data analyst, preparing passenger data. Filling `Age` with a mean of 28.0 ensures your model can predict transportation without gaps, helping space agencies prioritize rescue efforts!
Quiz Time!
Let’s test your data cleaning skills, students!
1. Why drop the `Name` column?
a) It has too many missing values
b) It’s unlikely to predict transportation
c) It’s a numerical feature
2. What does `fillna()` do?
a) Deletes missing values
b) Replaces missing values with a specified value
c) Counts missing values
Drop your answers in the comments
Cheat Sheet:
Handling Missing Values
- `df.drop(['Name'], axis=1)`: Drops the `Name` column (`axis=1` for columns).
- `df.column.fillna(value)`: Fills missing values in a column with a specified value.
- Tip: Use `df.column.mode()[0]` for mode and `df.column.mean()` for mean to compute exact values.
Did You Know?
Pandas’ `fillna()` method, widely used since 2010, makes imputation seamless—our project uses it to ensure a complete dataset!
Pro Tip:
Let’s fill the gaps in our Spaceship Titanic data to keep our model on course!
What’s Happening in This Code?
Let’s break it down like we’re repairing a spaceship’s hull:
- Drop Name Column: `df = df.drop(['Name'], axis=1)` removes the `Name` column, as it’s not predictive (200 missing, no clear link to `Transported`).
- Fill Missing Values:
- Categorical Columns (Modes):
- `HomePlanet`: Filled with `'Earth'` (201 missing), likely the mode.
- `CryoSleep`: Filled with `False` (217 missing), likely the mode.
- `Cabin`: Filled with `'G/734/S'` (199 missing), a specific value (possibly mode or median cabin).
- `Destination`: Filled with `'TRAPPIST-1e'` (182 missing), likely the mode.
- `VIP`: Filled with `False` (203 missing), likely the mode.
- Numerical Columns (Means):
- `Age`: Filled with 28.0 (179 missing), close to the mean of 28.827930 from `df.describe()`.
- `RoomService`: Filled with 224.0 (181 missing), close to the mean of 224.687617.
- `FoodCourt`: Filled with 458.0 (183 missing), close to the mean of 458.077120.
- `ShoppingMall`: Filled with 173.7 (208 missing), close to the mean of 173.729169.
- `Spa`: Filled with 311.1 (183 missing), close to the mean of 311.138778.
- `VRDeck`: Filled with 304.8 (188 missing), close to the mean of 304.854251.
- Verify: `df.isnull().sum()` checks for remaining missing values.
Handling Missing Values in Spaceship Titanic Dataset
Here’s the code we’re working with:
# Dropping 'Name' column since it's of no use
df = df.drop(['Name'], axis=1)
# Now filling out missing values
df.HomePlanet = df.HomePlanet.fillna('Earth')
df.CryoSleep = df.CryoSleep.fillna(False)
df.Cabin = df.Cabin.fillna('G/734/S')
df.Destination = df.Destination.fillna('TRAPPIST-1e')
df.Age = df.Age.fillna(28.0)
df.VIP = df.VIP.fillna(False)
df.RoomService = df.RoomService.fillna(224.0)
df.FoodCourt = df.FoodCourt.fillna(458.0)
df.ShoppingMall = df.ShoppingMall.fillna(173.7)
df.Spa = df.Spa.fillna(311.1)
df.VRDeck = df.VRDeck.fillna(304.8)
df.isnull().sum()
The Output:
Missing Values Check
Take a look at the uploaded image! The output shows:
- PassengerId: 0
- HomePlanet: 0
- CryoSleep: 0
- Cabin: 0
- Destination: 0
- Age: 0
- VIP: 0
- RoomService: 0
- FoodCourt: 0
- ShoppingMall: 0
- Spa: 0
- VRDeck: 0
- Transported: 0
Insight:
- All missing values are now filled—our dataset is complete! The imputation aligns with our strategy: modes for categorical (`Earth`, `False`, etc.) and means for numerical columns (rounded for simplicity, e.g., `Age` 28.0 vs. 28.827930).
- The values used are close to the actual means from `df.describe()` (e.g., `RoomService` 224.0 vs. 224.687617), ensuring minimal distortion.
- `Cabin`’s `'G/734/S'` fill suggests a specific choice—possibly the mode or a representative value. We’ll extract features like deck and side from `Cabin` later.
- With no missing values left, we’re ready for feature engineering and deeper analysis!
Next Steps:
We’ve sealed the data leaks—fantastic progress! Next, we’ll engineer features (e.g., extract group numbers from `PassengerId`, deck/side from `Cabin`), encode categorical variables, and visualize patterns to prepare for modeling.
Share your code block or ideas, and let’s keep this cosmic journey soaring. How do you feel about our clean dataset, viewers? Drop your thoughts in the comments, and let’s make this project a galactic game-changer together! 🌌🚀
A Stellar Launch:
Wrapping Up Part 1 of Spaceship Titanic AI Project!
What an incredible voyage we’ve embarked on, my stellar viewers and coding enthusiasts!
We’ve triumphantly concluded Part 1 of our "Spaceship Titanic AI Project" and I’m buzzing with excitement for what we’ve achieved on www.theprogrammarkid004.online
From loading our cosmic dataset to exploring its statistical summary, identifying missing values, cleaning `PassengerId`, and filling data gaps with mean and mode imputations, we’ve laid a rock-solid foundation for predicting which passengers were transported during the Spaceship Titanic disaster.
Whether you’ve been with me from Prague’s vibrant streets or coding passionately from across the galaxy, your enthusiasm has fueled this stellar start—let’s give ourselves a cosmic cheer! 🌌🚀
Reflecting on Our Galactic Beginnings
In Part 1, we’ve navigated the data universe with precision. We peeked at features like `HomePlanet`, `CryoSleep`, and spending habits, uncovered a 2-2.5% missingness rate, transformed `PassengerId` into a clean integer, and patched our dataset to perfection. These steps ensure our model will have a clear path to predict transportation outcomes, blending AI innovation with a sci-fi twist that’s out of this world!
Get Ready for Liftoff:
Part 2 Awaits!
But this is just the beginning—hold onto your seats, because Part 2 is where the real magic happens!
We’re diving into feature engineering and exploratory data analysis (EDA) to unlock hidden patterns. Imagine extracting group sizes from `PassengerId`, decoding deck and side from `Cabin`, and visualizing how `CryoSleep` or `RoomService` might sway transportation chances—get ready for mind-blowing charts and insights!
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’s your favorite moment from Part 1, viewers?
Drop your thoughts in the comments, and let’s gear up for an even more exciting Part 2 together—our galactic adventure is just heating up! 🌟🚀