👉Enhance Career App: Adding Profiles, Data, and AI Inside! 🌟

In the previous post, we built a simple yet powerful Career Guidance Advisor app that helps users find the right career paths based on their interests, skills, and goals.

Now, it's time to take things up a notch!

In this post, we’ll explore how to enhance the app by adding features like saving user profiles, incorporating detailed career data, and even using AI for smarter recommendations.

This is going to be fun and educational, especially for beginners!

Let’s get started.

Step 1: Saving User Profiles 📝

The first feature we’ll add is the ability to save user profiles.

This means users can input their preferences—like interests, skills, and goals—save them, and load them later.

This makes the app more personalized and user-friendly.

The Code:

import json

# Sample user profile data

user_profiles = {}

def save_profile(username, profile_data):

user_profiles[username] = profile_data

with open('profiles.json', 'w') as file:

json.dump(user_profiles, file)

print(f"Profile for {username} saved successfully!")

def load_profile(username):

with open('profiles.json', 'r') as file:

profiles = json.load(file)

return profiles.get(username, "Profile not found")

# Example usage

# Profile for John Doe

profile_john = {'interests': ['technology', 'design'], 'skills': ['Python', 'UX/UI'], 'goals': ['Become a software engineer']}

save_profile('john_doe', profile_john)

# Profile for Jane Smith

profile_jane = {'interests': ['science', 'healthcare'], 'skills': ['biology', 'data analysis'], 'goals': ['Pursue a career in data science']}

save_profile('jane_smith', profile_jane)

# Profile for Mark Jones

profile_mark = {'interests': ['art', 'creativity'], 'skills': ['graphic design', 'illustration'], 'goals': ['Work as a graphic designer']}

save_profile('mark_jones', profile_mark)

# Profile for Alice Brown

profile_alice = {'interests': ['finance', 'technology'], 'skills': ['accounting', 'Python'], 'goals': ['Become a financial analyst']}

save_profile('alice_brown', profile_alice)

# Profile for Bob White

profile_bob = {'interests': ['sports', 'nutrition'], 'skills': ['personal training', 'diet planning'], 'goals': ['Become a sports nutritionist']}

save_profile('bob_white', profile_bob)

# Loading and printing a specific profile

loaded_profile = load_profile('jane_smith')

print(loaded_profile)

Explanation:

• We start by using a dictionary, user_profiles, to store profile data.

• The save_profile function saves a user's profile to a file called profiles.json using Python's json module. This allows us to save and load user data easily.

• The load_profile function retrieves a user’s profile from the JSON file.

This functionality is key to making your app more personalized, as users can now save their data and retrieve it whenever they return to the app.

About Python’s JSON Module and File

Python's json module is a powerful tool for working with JSON (JavaScript Object Notation) data.

It allows you to easily convert Python objects, such as dictionaries and lists, into JSON format and vice versa.

This makes it simple to read from or write to JSON files, which are commonly used for storing configuration settings, data exchange between web services, or even storing user preferences in applications.

By using json, you can efficiently serialize (convert) Python data into a string that can be saved to a file or transmitted, and deserialize (parse) it back into Python objects when needed.

Step 2: Incorporating Detailed Career Data 🎓💼

Next, let's add more detailed career data.

This could include information like salary ranges, job market trends, educational requirements, and more.

The Code:

careers = {

'Software Engineer': {'salary_range': '80k-120k', 'job_trends': 'Growing', 'education': 'Bachelor\'s Degree in Computer Science'},

'Graphic Designer': {'salary_range': '40k-70k', 'job_trends': 'Stable', 'education': 'Bachelor\'s Degree in Graphic Design'},

'Data Scientist': {'salary_range': '90k-130k', 'job_trends': 'High Demand', 'education': 'Bachelor\'s or Master\'s in Data Science'}

}

def get_career_details(career_name):

return careers.get(career_name, "Career details not found")

# Example usage

career_details = get_career_details('Software Engineer')

print(career_details)

Explanation:

• We create a dictionary, careers, that holds detailed information about various career paths.

• The get_career_details function retrieves details about a specific career, which can be displayed to the user.

By including more detailed information, your app can provide comprehensive advice, helping users make informed career decisions.

Step 3: Adding AI for Smarter Recommendations 🤖💡

Finally, let’s sprinkle some AI into the mix.

By integrating a simple AI algorithm, your app can make smarter, more personalised career recommendations.

The Code:

from sklearn.neighbors import KNeighborsClassifier

from sklearn.preprocessing import LabelEncoder

# Sample data: interests, skills, goals mapped to career paths

data = [

['technology', 'coding', 'problem-solving', 'Software Engineer'],

['art', 'creativity', 'design', 'Graphic Designer'],

['math', 'statistics', 'analysis', 'Data Scientist']

]

def train_model(data):

# Extract features and labels

X = [d[:-1] for d in data] # Features: interests, skills, goals

y = [d[-1] for d in data] # Labels: career paths

# Initialize LabelEncoders for each feature column

encoders = [LabelEncoder() for _ in range(len(X[0]))]

# Transform each column in X

X_encoded = []

for col, encoder in zip(zip(*X), encoders):

X_encoded.append(encoder.fit_transform(col))

# Transpose back to original shape

X_encoded = list(zip(*X_encoded))

# Encode the labels

label_encoder = LabelEncoder()

y_encoded = label_encoder.fit_transform(y)

# Train the model

model = KNeighborsClassifier(n_neighbors=1)

model.fit(X_encoded, y_encoded)

# Return the trained model and the encoders

return model, encoders, label_encoder

def predict_career(model, encoders, label_encoder, user_input):

# Encode user input using the same encoders

user_input_encoded = [encoder.transform([feature])[0] for feature, encoder in zip(user_input, encoders)]

predicted_label = model.predict([user_input_encoded])[0]

# Decode the predicted label back to the career path

return label_encoder.inverse_transform([predicted_label])[0]

# Example usage

model, encoders, label_encoder = train_model(data)

user_input = ['technology', 'coding', 'problem-solving']

predicted_career = predict_career(model, encoders, label_encoder, user_input)

print(predicted_career)

Explanation:

• We use the KNeighborsClassifier from the sklearn library to create a simple AI model that maps user inputs (interests, skills, goals) to career paths.

• The train_model function trains the model with sample data.

• Label Encoding: The LabelEncoder converts the categorical string data into numeric data. Each unique string is assigned a unique integer.

• Fitting and Transforming: The features (X) and labels (y) are encoded using the fit_transform() method, which both fits the encoder to the data and transforms it.

• Prediction: For prediction, the user input is also transformed using the same encoders that were used to train the model.

• The predict_career function uses the trained model to predict the best career for a user based on their inputs.

With AI, your app becomes even more powerful, providing users with smarter, data-driven recommendations.

Wrapping Up 🎉

In this post, you learned how to enhance your Career Guidance Advisor app by adding user profiles, incorporating detailed career data, and integrating AI for smarter recommendations.

These features not only make the app more useful but also more engaging and personalized for users.

Coding with a Smile 🤣 😂

Type Error Tantrums:

Type errors are like trying to fit a square peg into a round hole.

Python’s there to remind you, “Hey, that’s not going to work. Let’s try something else.”

Recommended Resources 📚

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What’s Next? 📅

In the next post, we'll dive deeper into the AI example on KNeighborsClassifier breaking it down step by step to make it easy to understand.

We'll cover how the code uses machine learning techniques to predict career paths based on user input, focusing on how categorical data is converted to numerical form and how the K-Nearest Neighbors algorithm works in this context.

Whether you're new to AI or looking to strengthen your skills, this post will guide you through the process, ensuring you grasp the key concepts and feel confident applying them in your own projects.

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