Autism Prediction using Ai (Part-2)
Unveiling Insights
Welcome to Part 2 of Our Autism Prediction Project!
Hello, my incredible viewers and students! I’m absolutely thrilled to welcome you back to Part 2 of our "Autism Prediction Classification Project".
After a fantastic Part 1 where we loaded our autism dataset, cleaned the ethnicity column by encoding ‘?’ as ‘others’, and balanced our target Class/ASD (now 639:639), we’re ready to dive deeper with feature engineering and exploratory data analysis (EDA). Today, we’ll transform our features to make them model-ready and uncover hidden patterns that could help predict autism spectrum disorder (ASD) with precision.
Whether you’re joining me from Vancouver British Columbia Canada’s bustling streets or bringing your passion for AI from across the globe, let’s blend data science with empathy to create a tool that supports early diagnosis and care. Grab your coding gear, and let’s make this journey even more impactful—cheers to Part 2! 🌟🚀
Exploring Connections
EDA with Country of Residence
After balancing our target column Class/ASD in Part 1 (now 639:639), we’re kicking off Part 2 with exploratory data analysis (EDA) to uncover patterns that can enhance our autism prediction model. This code block visualizes the distribution of Class/ASD across countries of residence using a count plot, helping us understand how location might influence ASD prevalence.
Why EDA with Country of Residence Matters
Understanding how Class/ASD varies by contry_of_res can reveal geographical patterns in autism prevalence, which might reflect differences in diagnosis rates or environmental factors. For researchers in Geneva, Switzerland this insight could guide localized interventions, ensuring our model supports diverse communities.
What to Expect in This Step
In this step, we’ll:
Create a count plot to visualize Class/ASD distribution across countries of residence.
Analyze the output to identify trends or imbalances.
Prepare for feature engineering by noting categorical variables to encode.
Get ready to explore our data with a fresh perspective—our journey is getting even more insightful!
Fun Fact:
Global Autism Trends!
Did you know autism diagnosis rates vary globally? Studies show higher rates in countries like the U.S. due to better awareness—our count plot might reveal similar trends in our dataset!
Real-Life Example
Imagine you’re a public health official Boston Massachusetts, planning autism awareness campaigns. This count plot could show if certain countries have higher ASD rates, helping you target resources where they’re needed most!
Quiz Time!
Let’s test your EDA skills, students!
What does a count plot with hue='Class/ASD' show?
a) Correlation between features
b) Count of ASD vs. no ASD for each country
c) Average age per country
Why rotate the x-axis labels?
a) To make the plot colorful
b) To prevent overlapping text
c) To change the data
Drop your answers in the comments
Cheat Sheet:
Visualizing with Count Plots
sns.countplot(data, x, hue): Plots counts of categories in x, split by hue.
plt.figure(figsize=(15,9)): Sets a large plot size for readability.
plt.xticks(rotation=90): Rotates x-axis labels to avoid overlap.
Did You Know?
Count plots, part of Seaborn since its release in 2014, are a go-to for categorical data analysis—perfect for exploring how ASD varies by country in our autism project!
Pro Tip
How does autism prevalence vary by country? Let’s explore with a count plot.
What’s Happening in This Code?
Let’s break it down like we’re crafting a perfect blend:
Figure Size: plt.figure(figsize=(15,9)) creates a large 15x9-inch plot for clarity with many countries.
Count Plot: sns.countplot(data=df, x='contry_of_res', hue='Class/ASD') plots the count of individuals from each country, with bars split by Class/ASD (0: no ASD, 1: ASD).
Label Rotation: plt.xticks(rotation=90) rotates x-axis labels to prevent overlap, given the many country names.
Display: plt.show() renders the plot.
Visualizing Country of Residence vs. Class/ASD
Here’s the code we’re working with:
plt.figure(figsize=(15,9))
sns.countplot(data=df,x='contry_of_res',hue='Class/ASD')
plt.xticks(rotation=90)
plt.show()
The Output:
Count Plot of Country of Residence vs. Class/ASD
Take a look at the uploaded image! The count plot shows the distribution of Class/ASD across countries of residence (contry_of_res):
X-Axis (Country of Residence): Lists unique countries (note the typo: ‘contry’ instead of ‘country’). There are 62 countries, including:
United States: The tallest bars, with counts exceeding 200 for both 0 (no ASD) and 1 (ASD).
United Kingdom, India, New Zealand, Australia: Moderate bars, with counts around 50-100 each for both classes.
Canada, Jordan, South Africa, Brazil, Pakistan: Smaller bars, with counts around 20-50.
Many others like Afghanistan, Italy, Netherlands, Iran, Russia, UAE, etc., with counts below 20, some as low as 1-5.
Y-Axis (Count): The height of each bar, ranging from 0 to over 200, showing the number of individuals per class.
Hue (Class/ASD): Each country has two bars:
Blue (0 - No ASD): Represents individuals without ASD.
Orange (1 - ASD): Represents individuals with ASD.
Key Observation: After oversampling in Part 1, each country shows roughly equal counts for 0 and 1 (e.g., United States has ~200 for both), reflecting our balanced Class/ASD (639:639 total).
Insight: The plot confirms our target balance post-oversampling, with each country showing nearly equal representation of ASD (1) and no ASD (0). The United States dominates the dataset, followed by the UK and India, while smaller countries like Pakistan (relevant to Lahore viewers!) have fewer samples. This distribution suggests contry_of_res might not be a strong predictor due to the balanced split across all countries post-oversampling, but it highlights global diversity in our data. We’ll need to encode this categorical feature (with 62 unique values) next, possibly grouping smaller countries into an ‘others’ category to simplify our model.
Next Steps:
We’ve gained a global perspective—great insight! Next, we’ll encode categorical features like contry_of_res, gender, and ethnicity, and continue our EDA to uncover more patterns.
Crafting the Perfect Blend: Feature Engineering with Age Groups
After exploring the distribution of Class/ASD across countries of residence, we’re now diving into feature engineering to enhance our autism prediction model. This code block introduces a custom function to group ages into meaningful categories (Baby, Kid, Teenager, Young, Senior/OLD), creating a new feature ageGroup that could reveal age-related patterns in ASD prevalence.
Why Feature Engineering with Age Matters
Transforming the continuous age into categorical ageGroup can help our model capture age-specific trends in autism, which might vary across developmental stages. For clinicians in Europe, this could highlight when ASD traits are most detectable, supporting targeted screenings.
What to Expect in This Step
In this step, we’ll:
Define a function convertAge to categorize ages into groups.
Apply it to create a new ageGroup column in our dataset.
Preview the updated DataFrame to see the new feature in action.
Get ready to engineer a feature that adds depth to our analysis—our journey is getting even more insightful!
Fun Fact:
Age and Autism Diagnosis!
Did you know autism is often diagnosed between ages 2-4, but traits can appear earlier? Grouping ages into categories like ours can help models pinpoint these critical windows—our feature engineering is right on track!
Real-Life Example
Imagine you’re a child psychologist assessing a patient. A model using ageGroup might show that ‘Kid’ (4-11) has higher ASD likelihood, guiding you to focus screening efforts on that age range!
Quiz Time!
Let’s test your feature engineering skills, students!
What does convertAge do?
a) Deletes the age column
b) Categorizes age into groups like ‘Kid’ or ‘Teenager’
c) Calculates age averages
Why create ageGroup?
a) To confuse the model
b) To capture age-related patterns for prediction
c) To reduce dataset size
Drop your answers in the comments
Cheat Sheet:
Feature Engineering with Functions
def function_name(parameter): Defines a custom function (e.g., convertAge).
df['column'].apply(function): Applies the function to each value in the column.
Tip: Use clear age ranges to align with domain knowledge (e.g., autism diagnosis stages).
Did You Know?
Feature engineering, a cornerstone of machine learning since the 1990s, often boosts model performance by 10-20%—our ageGroup could be a game-changer for autism prediction!
Pro Tip
Let’s engineer a new feature! How will age groups like ‘Kid’ or ‘Senior’ shape our autism predictions?
What’s Happening in This Code?
Let’s break it down like we’re crafting a fine vintage:
Function Definition: def convertAge(age) creates a function that categorizes age into groups:
< 4: ‘Baby’
< 12: ‘Kid’
< 18: ‘Teenager’
< 40: ‘Young’
>= 40: ‘Senior/OLD’
Apply Function: df['age'].apply(convertAge) applies the function to each value in the age column, creating a new ageGroup column.
Preview: df.head() displays the first 5 rows to show the new feature.
Creating Age Groups with Feature Engineering
Here’s the code we’re working with:
#Creating a function that makes groups by taking age as parameter
def convertAge(age):
if age <4:
return 'Baby'
elif age < 12:
return 'Kid'
elif age < 18:
return 'Teenager'
elif age < 40:
return 'Young'
else:
return 'Senior/OLD'
df['ageGroup'] = df['age'].apply(convertAge)
df.head()
The Output:
Updated DataFrame with Age Group
Take a look at the uploaded image! The output of df.head() shows the first 5 rows, including the new ageGroup column:
Columns (from the image):
age_desc: Always ‘18 and more’, likely redundant with age.
relation: All ‘Self’, indicating self-reported data.
Class/ASD: 1 for all 5 rows (ASD), reflecting our balanced dataset post-oversampling.
ageGroup: New column with age-based categories:
Row 1: ‘Teenager’ (age likely < 18, but age column not visible—assumed from context).
Row 2: ‘Senior/OLD’ (age likely >= 40).
Row 3: ‘Young’ (age likely < 40 but >= 18).
Row 4: ‘Kid’ (age likely < 12).
Row 5: ‘Young’ (age likely < 40 but >= 18).
age (not shown but implied): The original numerical age values that convertAge processed.
Insight: The ageGroup feature successfully categorizes ages into meaningful groups, aligning with developmental stages relevant to autism (e.g., ‘Kid’ for early diagnosis). Since age isn’t visible in the output, we infer the categories based on the function logic and typical dataset ranges (e.g., 27.0, 24.0 from Part 1 would be ‘Young’). The consistency of Class/ASD as 1 in these rows suggests our oversampling focused on ASD cases, which we’ll verify across the dataset. This new feature will help us explore age-related ASD patterns in our next EDA step!
Next Steps:
We’ve engineered a brilliant new feature—time to explore its impact! Next, we’ll perform EDA to analyze ageGroup’s correlation with Class/ASD, and continue encoding other categorical variables.
What do you think of this age grouping, viewers? Drop your thoughts
Uncovering Age Insights:
EDA with Age Groups
After engineering a new ageGroup feature to categorize ages into Baby, Kid, Teenager, Young, and Senior/OLD, we’re now diving deeper into exploratory data analysis (EDA) to explore how these groups relate to our target Class/ASD. This code block creates a count plot to visualize the distribution of ASD (0: no ASD, 1: ASD) across age groups, revealing potential patterns in autism prevalence.
Cheers to data-driven compassion! 🌟🚀
Why EDA with Age Groups Matters
Analyzing ageGroup against Class/ASD can highlight age-related trends in autism, which might align with diagnosis windows (e.g., early childhood). For educators or clinicians in Amsterdam this could guide targeted screenings, ensuring no age group is overlooked in our predictive model.
What to Expect in This Step
In this step, we’ll:
Create a count plot to show the count of ASD vs. no ASD across each age group.
Interpret the output to identify any age-specific patterns in autism prevalence.
Set the stage for further feature engineering or modeling.
Get ready to dive into the age-based story of our data—our journey is heating up!
Fun Fact:
Age and Autism Diagnosis!
Did you know autism is most commonly diagnosed in children aged 2-4, but traits can persist or emerge later? Our age group analysis might reflect these critical stages—let’s see what the data reveals!
Real-Life Example
Imagine you’re a school counselor assessing students. A count plot showing higher ASD rates in the ‘Kid’ group could prompt earlier interventions, thanks to insights from our model!
Quiz Time!
Let’s test your EDA skills, students!
What does hue=df['Class/ASD'] do in the count plot?
a) Changes the plot color
b) Splits bars by ASD (0 or 1)
c) Shows age averages
Why is ageGroup useful here?
a) It deletes age data
b) It groups ages for pattern analysis
c) It predicts directly
Drop your answers in the comments
Cheat Sheet:
Creating Count Plots
sns.countplot(x=..., hue=...): Plots counts of x categories, split by hue.
plt.show(): Displays the plot.
Tip: Add plt.title() and plt.xlabel() for better context in future plots.
Did You Know?
Count plots became a favorite in EDA with Seaborn’s release in 2014, making it easy to spot categorical trends—perfect for our age group analysis!
Pro Tip:
Which age group shows more autism? Let’s explore with a count plot of ageGroup!
What’s Happening in This Code?
Let’s break it down like we’re tasting a new vintage:
Count Plot: sns.countplot(x=df.ageGroup, hue=df['Class/ASD']) creates a bar plot where:
x=df.ageGroup sets the x-axis to our engineered age groups (Baby, Kid, Teenager, Young, Senior/OLD).
hue=df['Class/ASD'] splits each bar into two colors: blue for 0 (no ASD) and orange for 1 (ASD).
Display: plt.show() renders the plot.
Visualizing Age Group vs. Class/ASD
Here’s the code we’re working with:
sns.countplot(x=df.ageGroup, hue=df['Class/ASD'])
plt.show()
The Output:
Count Plot of Age Group vs. Class/ASD
Take a look at the uploaded image! The count plot shows the distribution of Class/ASD across age groups:
X-Axis (ageGroup): Categories from our convertAge function:
Baby: Very low count (near 0).
Kid: Moderate count.
Teenager: Moderate count.
Young: Highest count.
Senior/OLD: Moderate to high count.
Y-Axis (Count): The height of each bar, ranging from 0 to around 300, showing the number of individuals per age group and class.
Hue (Class/ASD):
Blue (0 - No ASD): Represents individuals without ASD.
Orange (1 - ASD): Represents individuals with ASD.
Key Observations:
Young: The tallest bars, with both 0 and 1 around 250-300 each, showing the largest group after oversampling.
Senior/OLD: Significant bars, with both 0 and 1 around 200-250.
Teenager: Moderate bars, with both 0 and 1 around 100-150.
Kid: Smaller bars, with both 0 and 1 around 50-100.
Baby: Negligible bars, with counts near 0 for both classes.
Insight: The plot reflects our balanced Class/ASD (639:639) post-oversampling, with each age group showing roughly equal counts for 0 and 1. The ‘Young’ (18-39) and ‘Senior/OLD’ (40+) groups dominate, likely due to the original dataset’s age range (e.g., 24-36 from Part 1), with fewer ‘Baby’ (<4) and ‘Kid’ (4-11) samples—possibly due to limited data for younger ages or self-reporting bias (all relation as ‘Self’). This suggests ageGroup might not strongly differentiate ASD vs. no ASD on its own, but it confirms our oversampling worked across all groups. Next, we’ll explore correlations to see if ageGroup interacts with other features like result or ethnicity.
Next Steps:
We’ve uncovered age group trends—fascinating insights! Next, we’ll perform correlation analysis to see how ageGroup and other features relate to Class/ASD, and continue our feature engineering journey. What did you notice about the age groups, viewers?
Drop your thoughts in the comments🌟🚀
Enhancing Our Model
Adding New Features
Welcome back, my incredible viewers and students, to the next exciting step of Part 2 in our "Autism Prediction Classification Project".
After exploring the distribution of Class/ASD across age groups, we’re now diving deeper into feature engineering by creating two new features: sum_score (total of A1-A10 scores) and Pak (a combination of austim, used_app_before, and jaundice). These additions will help our model capture more patterns for predicting autism spectrum disorder (ASD).
Why Adding Features Matters
New features like sum_score and Pak can help our model identify stronger patterns in autism prediction. For healthcare providers in Oslo, Norway these features might highlight key behavioral or medical history trends, improving early detection of ASD.
What to Expect in This Step
In this step, we’ll:
Define a function add_features to create two new columns: sum_score (sum of A1-A10 scores) and Pak (a derived feature).
Apply the function to our dataset and preview the updated DataFrame.
Set the stage for further EDA or encoding.
Get ready to enrich our data with new insights—our journey is getting even more powerful!
Fun Fact:
Feature Engineering in Medicine!
Did you know feature engineering often boosts medical model accuracy by 5-15%? Summing behavioral scores, as we’re doing with sum_score, mirrors how clinicians calculate screening totals for autism diagnosis!
Real-Life Example
Imagine you’re a researcher developing a screening tool. A feature like sum_score could flag high-risk individuals (e.g., scores ≥ 6), while Pak might reveal if family history and medical factors combined increase ASD likelihood!
Quiz Time!
Let’s test your feature engineering skills, students!
What does sum_score represent?
a) The average of A1-A10 scores
b) The total of A1-A10 scores
c) The count of missing scores
Why create a feature like Pak?
a) To delete data
b) To combine medical history factors for better prediction
c) To reduce columns
Drop your answers in the comments
Cheat Sheet:
Feature Engineering with Functions
df['new_column'] = 0: Initializes a new column with zeros.
df.loc[:, 'start':'end']: Selects a range of columns (e.g., A1-A10).
df['col1'] + df['col2']: Combines columns element-wise.
Did You Know?
Combining features, as we’re doing with Pak, became a popular technique in the 2000s with the rise of ensemble models—perfect for our autism prediction task!
Pro Tip:
Let’s supercharge our data with new features! What will sum_score and Pak reveal about autism?
What’s Happening in This Code?
Let’s break it down like we’re crafting a perfect recipe:
Function Definition: def add_features(data) defines a function to add new features:
df['sum_score'] = 0: Initializes a new column sum_score with zeros.
df.loc[:, 'A1_Score':'A10_Score'].columns: Selects columns from A1_Score to A10_Score.
for col in ...: Loops through these columns, adding each score to sum_score.
df['Pak'] = df['austim'] + df['used_app_before'] + df['jaundice']: Creates Pak by adding austim, used_app_before, and jaundice (though we’ll note an issue with this step).
Apply Function: df = add_features(df) applies the function to the dataset.
Preview: df.head() shows the first 5 rows with the new columns.
Adding New Features to the Dataset
Here’s the code we’re working with:
def add_features(data):
#creating a columnw with all values zero
df['sum_score'] = 0
for col in df.loc[:, 'A1_Score':'A10_Score'].columns:
df['sum_score'] += df[col]
#Now creating a random data using the below columns
df['Pak'] = df['austim'] + df['used_app_before'] + df['jaundice']
return data
df = add_features(df)
df.head()
The Output:
Updated DataFrame with New Features
Take a look at the uploaded image! The output of df.head() shows the first 5 rows with the new columns sum_score and Pak:
Columns (from the image):
A1_Score to A10_Score: Binary scores (0 or 1) for autism screening questions (e.g., Row 1: 1, 1, 0, 0, 1, 1, 1, 1, 0, 1).
sum_score: Total of A1-A10 scores:
Row 1: 7 (1+1+0+0+1+1+1+1+0+1).
Row 2: 8 (1+1+0+1+1+1+1+0+1+1).
Row 3: 4 (1+0+0+0+1+0+0+1+0+1).
Row 4: 8 (1+1+0+1+1+1+1+0+1+1).
Row 5: 4 (1+0+0+1+0+0+0+1+0+1).
Pak: Supposed to be the sum of austim, used_app_before, and jaundice, but these are categorical (‘yes’/’no’):
Row 1: ‘yesyesno’ (concatenated strings: ‘yes’ + ‘yes’ + ‘no’).
Row 2: ‘yesnono’.
Row 3: ‘yesnono’.
Row 4: ‘yesnono’.
Row 5: ‘yesnono’.
Class/ASD, ageGroup, etc.: Other columns remain unchanged.
Insight: The sum_score column correctly sums the A1-A10 scores, aligning with the result column from Part 1 (e.g., Row 1’s sum_score of 7 matches earlier patterns). However, Pak has an issue: austim, used_app_before, and jaundice are strings (‘yes’/’no’), so adding them concatenates instead of summing (e.g., ‘yes’ + ‘yes’ + ‘no’ = ‘yesyesno’). To fix this, we’ll need to encode these columns as binary (0/1) before summing.
Despite this, sum_score is a valuable feature, likely correlating strongly with Class/ASD since it’s derived from screening questions. We’ll address Pak in the next step!
Next Steps:
We’ve added new features—great progress, with a tweak needed! Next, we’ll fix Pak by encoding austim, used_app_before, and jaundice, then continue our EDA to explore correlations with Class/ASD.
What do you think of these new features, viewers? Drop your thoughts in the comments, and let’s make this project a game-changer together! 🌟🚀
Decoding Behavioral Scores:
EDA with Sum Score
After adding new features like sum_score (the total of A1-A10 scores) and identifying a fix needed for Pak, we’re now diving deeper into exploratory data analysis (EDA) to see how sum_score relates to our target Class/ASD. This code block creates a count plot to visualize the distribution of ASD (0: no ASD, 1: ASD) across sum_score values, revealing how screening totals might predict autism spectrum disorder (ASD).
Why EDA with Sum Score Matters
The sum_score (sum of A1-A10 screening questions) reflects the intensity of autism-related traits, making it a potential key predictor for Class/ASD. For clinicians, understanding this relationship could highlight score thresholds for ASD risk, aiding early diagnosis and support.
What to Expect in This Step
In this step, we’ll:
Create a count plot to show the count of ASD vs. no ASD across each sum_score value.
Analyze the output to identify trends linking behavioral scores to autism.
Prepare for encoding and modeling by noting influential features.
Get ready to explore a critical feature—our journey is revealing powerful connections!
Fun Fact
Autism Screening Scores!
Did you know autism screening tools like the AQ-10 (basis for A1-A10) often use a threshold of 6 or higher to flag ASD risk? Our sum_score analysis might confirm this threshold in our data!
Real-Life Example
Imagine you’re a pediatrician, screening a child. A count plot showing high sum_score values (e.g., 7-10) linked to ASD could prompt you to recommend further evaluation, ensuring timely support for the family!
Quiz Time!
Let’s test your EDA skills, students!
What does sum_score represent in this plot?
a) The average of A1-A10 scores
b) The total of A1-A10 scores
c) The count of yes/no answers
Why use hue=df['Class/ASD']?
a) To change colors
b) To split bars by ASD (0 or 1)
c) To show age groups
Drop your answers in the comments
Cheat Sheet:
Visualizing with Count Plots
sns.countplot(x=..., hue=...): Plots counts of x values, split by hue.
plt.show(): Displays the plot.
Tip: Add plt.title() and plt.xlabel() for clarity in future plots.
Did You Know?
The AQ-10 screening tool, which inspires our A1-A10 scores, was developed in 2012 by the University of Cambridge—our sum_score analysis taps into this clinical standard!
Pro Tip:
Can behavioral scores predict autism? Let’s explore sum_score with a count plot!
What’s Happening in This Code?
Let’s break it down like we’re savoring a fine detail:
Count Plot: sns.countplot(x=df.sum_score, hue=df['Class/ASD']) creates a bar plot where:
x=df.sum_score sets the x-axis to sum_score values (0 to 10, as A1-A10 are binary).
hue=df['Class/ASD'] splits each bar into two colors: blue for 0 (no ASD) and orange for 1 (ASD).
Display: plt.show() renders the plot.
Visualizing Sum Score vs. Class/ASD
Here’s the code we’re working with:
sns.countplot(x=df.sum_score,hue=df['Class/ASD'])
plt.show()
The Output:
Count Plot of Sum Score vs. Class/ASD
Take a look at the uploaded image! The count plot shows the distribution of Class/ASD across sum_score values:
X-Axis (sum_score): Ranges from 0 to 10, representing the total of A1-A10 scores (each score is 0 or 1, so the sum ranges from 0 to 10).
0 to 3: Low scores, indicating fewer autism-related traits.
4 to 6: Moderate scores, a potential transition zone.
7 to 10: High scores, often associated with higher ASD likelihood.
Y-Axis (Count): The height of each bar, ranging from 0 to around 300, showing the number of individuals per sum_score and class.
Hue (Class/ASD):
Blue (0 - No ASD): Represents individuals without ASD.
Orange (1 - ASD): Represents individuals with ASD.
Key Observations:
Scores 0-3: Mostly blue (0), with counts around 50-150, showing most individuals with low scores don’t have ASD.
Score 4: A transition point, with blue and orange bars roughly equal (around 100 each).
Score 5: Orange (1) starts to dominate slightly, with counts around 150 vs. 100 for blue.
Scores 6-10: Mostly orange (1), with counts around 150-300, showing high scores strongly linked to ASD.
Score 8: Peaks for orange (1) at around 300, indicating many ASD individuals score 8.
Insight: The sum_score feature is a strong predictor for Class/ASD! Low scores (0-3) are associated with no ASD (0), while high scores (6-10) are heavily linked to ASD (1), aligning with clinical thresholds (e.g., AQ-10 often flags scores ≥ 6 for ASD risk). The transition at scores 4-5 reflects our balanced dataset (639:639 post-oversampling), but the clear separation suggests sum_score will be a key feature for our model. Since sum_score matches the result column from Part 1, we might drop one to avoid redundancy. This pattern also validates our oversampling, as ASD cases now dominate higher scores, ensuring our model won’t miss them!
Next Steps:
We’ve found a powerful predictor—great work! Next, we’ll fix the Pak feature by encoding austim, used_app_before, and jaundice, then encode other categorical variables like ethnicity and contry_of_res.
A Transformative Journey: Wrapping Up Part 2 of Our Autism Prediction Project!
What an extraordinary ride we’ve had together, my amazing viewers and students! We’ve just wrapped up Part 2 of our "Autism Prediction Classification Project" and I’m bursting with pride over our incredible progress.
We kicked off with exploratory data analysis (EDA), uncovering global patterns with contry_of_res and age-related trends with ageGroup, confirming our balanced Class/ASD (639:639). Then, we dove into feature engineering, crafting ageGroup to group ages into Baby, Kid, Teenager, Young, and Senior/OLD, and added sum_score (total A1-A10 scores) and Pak (a fix-needed combo of medical history factors). Our EDA with sum_score revealed a striking link—high scores (6-10) strongly predict ASD—setting a solid foundation for our model.
The Adventure Continues:
Get Ready for Part 3!
Hold onto your excitement because Part 3 is about to take our project to new heights! On our Website, www.theprogrammarkid004.online we’ll:
More Feature Engineering: Apply label encoding to transform categorical variables like ethnicity and contry_of_res into model-ready formats.
Deepened EDA: Check distributions of key features and compute correlations to pinpoint the strongest predictors for Class/ASD.
And Beyond: Prepare our data for modeling, explore advanced techniques, and evaluate our autism prediction system.
Make sure to subscribe www.youtube.com/@cognitutorai hit that notification bell, and join our community of compassionate coders.
Whether you’re in the USA or dreaming of making a global impact, let’s keep this meaningful journey flowing. What was your favorite discovery—sum_score’s predictive power or ageGroup trends? Drop it in the comments, and tell me what you’re most excited for in Part 3—I can’t wait to build this life-changing model with you! 🌟🚀