Which python library is not used specifically for data visualization?
Anonymous Quiz
8%
Matplotlib
17%
Plotly
58%
Numpy
17%
Seaborn
been doin some exercises to figure out some of Pandas functions and tbh its fun also a lil bit confusing 🙂
🔥 3 Days of Matplotlib Mastery! 🎨📊
The past three days have been a deep dive into Matplotlib, and wow—visualizing data has never felt this smooth! From basic plots to working with pandas DataFrames and NumPy arrays, here’s what I’ve unlocked:
🔹 Line Plots – Perfect for tracking trends over time.
🔹 Scatter Plots – Ideal for revealing relationships between variables.
🔹 Bar Charts – The go-to for categorical comparisons.
🔹 Histograms – Great for understanding data distributions.
🔹 Subplots – Because one plot is never enough!
💡 I also explored two ways to create subplots:
✅
✅
The Matplotlib workflow now makes total sense:
1️⃣ Import Matplotlib & NumPy
2️⃣ Load or generate data
3️⃣ Pick the right plot & customize
4️⃣ Display or save the figure
I’m starting to see why data visualization is so powerful! 📊🔥 Let me know if you want to see some of the code examples I worked on. 🚀
#Matplotlib #DataScience
The past three days have been a deep dive into Matplotlib, and wow—visualizing data has never felt this smooth! From basic plots to working with pandas DataFrames and NumPy arrays, here’s what I’ve unlocked:
🔹 Line Plots – Perfect for tracking trends over time.
🔹 Scatter Plots – Ideal for revealing relationships between variables.
🔹 Bar Charts – The go-to for categorical comparisons.
🔹 Histograms – Great for understanding data distributions.
🔹 Subplots – Because one plot is never enough!
💡 I also explored two ways to create subplots:
✅
plt.subplot() – Quick & simple. ✅
plt.subplots() – Gives more control over layout. The Matplotlib workflow now makes total sense:
1️⃣ Import Matplotlib & NumPy
2️⃣ Load or generate data
3️⃣ Pick the right plot & customize
4️⃣ Display or save the figure
I’m starting to see why data visualization is so powerful! 📊🔥 Let me know if you want to see some of the code examples I worked on. 🚀
#Matplotlib #DataScience
Next Chapter: Data Science Foundations! 📊
The next few weeks will be all about pandas, Matplotlib, and NumPy**—the powerhouse trio for data analysis and visualization in Python! 🔥
💡 **Goals:
✅ Master data manipulation with pandas 🏗
✅ Get comfortable with numerical operations using NumPy
✅ Bring data to life with Matplotlib 🎨
To make things even more exciting, I’ll be working on small projects with real-world data—because the best way to learn is by doing! 💪
If you have any cool project ideas or datasets I should explore, Let’s build something awesome. 🚀
#Python #DataScience #Pandas #NumPy #Matplotlib #LearningByDoing
The next few weeks will be all about pandas, Matplotlib, and NumPy**—the powerhouse trio for data analysis and visualization in Python! 🔥
💡 **Goals:
✅ Master data manipulation with pandas 🏗
✅ Get comfortable with numerical operations using NumPy
✅ Bring data to life with Matplotlib 🎨
To make things even more exciting, I’ll be working on small projects with real-world data—because the best way to learn is by doing! 💪
If you have any cool project ideas or datasets I should explore, Let’s build something awesome. 🚀
#Python #DataScience #Pandas #NumPy #Matplotlib #LearningByDoing
Basics of Machine Learning 👇👇
1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
i was playing with some data and trying ML models, yup it feels awesome 🙂
📊 Intro to Scikit-Learn (sklearn) 🚀
Scikit-Learn is a powerful Python library for machine learning, making it easy to build and evaluate models. Here's a quick workflow to get started:
1️⃣ End-to-End Workflow: Follow a structured approach for ML success.
2️⃣ Getting the Data Ready: Preprocess data by handling missing values, scaling, and encoding.
3️⃣ Choosing the Right Estimator: Select the best algorithm for your problem (e.g., regression, classification).
4️⃣ Fit the Model: Train your model and make predictions on new data.
5️⃣ Evaluate the Model: Use metrics (e.g., accuracy, RMSE) to assess performance.
6️⃣ Improve the Model: Tune hyperparameters or try advanced techniques like ensemble methods.
7️⃣ Save & Load Models: Use
8️⃣ Putting It All Together; Combine everything into a robust ML pipeline!
👉 Dive into Scikit-Learn to turn your data into insights!
#ScikitLearn #MachineLearning #Python
Scikit-Learn is a powerful Python library for machine learning, making it easy to build and evaluate models. Here's a quick workflow to get started:
1️⃣ End-to-End Workflow: Follow a structured approach for ML success.
2️⃣ Getting the Data Ready: Preprocess data by handling missing values, scaling, and encoding.
3️⃣ Choosing the Right Estimator: Select the best algorithm for your problem (e.g., regression, classification).
4️⃣ Fit the Model: Train your model and make predictions on new data.
5️⃣ Evaluate the Model: Use metrics (e.g., accuracy, RMSE) to assess performance.
6️⃣ Improve the Model: Tune hyperparameters or try advanced techniques like ensemble methods.
7️⃣ Save & Load Models: Use
pickle or joblib to persist trained models. 8️⃣ Putting It All Together; Combine everything into a robust ML pipeline!
👉 Dive into Scikit-Learn to turn your data into insights!
#ScikitLearn #MachineLearning #Python
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Dealing with Missing Data in Python
Missing data? No problem
I explore 2 powerful methods to handle it:
1️⃣ Filling Missing Data with NumPy/Pandas
✔️ Use .fillna() to replace missing values.
Replace categorical values with "missing".
Replace numerical values with a constant or the column's mean.
2️⃣ Filling Missing Data with Scikit-Learn
✔️ Use SimpleImputer for flexible, scalable imputation.
Define strategies like constant (e.g., "missing", 4) or mean.
Handle categorical, numerical, and mixed datasets easily.
🔗 Combine Scikit-learn with ColumnTransformer to handle multi-type columns in one step.
📊 Master these methods and make your data analysis more robust!
#Python #DataScience #ScikitLearn #NumPy
Missing data? No problem
I explore 2 powerful methods to handle it:
1️⃣ Filling Missing Data with NumPy/Pandas
✔️ Use .fillna() to replace missing values.
Replace categorical values with "missing".
Replace numerical values with a constant or the column's mean.
2️⃣ Filling Missing Data with Scikit-Learn
✔️ Use SimpleImputer for flexible, scalable imputation.
Define strategies like constant (e.g., "missing", 4) or mean.
Handle categorical, numerical, and mixed datasets easily.
🔗 Combine Scikit-learn with ColumnTransformer to handle multi-type columns in one step.
📊 Master these methods and make your data analysis more robust!
#Python #DataScience #ScikitLearn #NumPy
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