Pie Chart Using Pandas

15 Best Project Ideas for Python : 🐍

🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter

🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator

🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis


Here you can find essential Python Resources👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

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MIT's "Machine Learning" lecture notes

PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

Use Python to turn messy data into valuable insights!

Here are the main functions you need to know:

1. 𝗱𝗿𝗼𝗽𝗻𝗮(): Clean up your dataset by removing missing values. Use df.dropna() to eliminate rows or columns with NaNs and keep your data clean.
   
2. 𝗳𝗶𝗹𝗹𝗻𝗮(): Replace missing values with a specified value or method. With the help of df.fillna(value) you maintain data integrity without losing valuable information.
   
3. 𝗱𝗿𝗼𝗽_𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀(): Ensure your data is unique and accurate. Use df.drop_duplicates() to remove duplicate rows and avoid skewing your analysis by aggregating redundant data.
   
4. 𝗿𝗲𝗽𝗹𝗮𝗰𝗲(): Substitute specific values throughout your dataset. The function df.replace(to_replace, value) allows for efficient correction of errors and standardization of data.
   
5. 𝗮𝘀𝘁𝘆𝗽𝗲(): Convert data types for consistency and accuracy. Use the cast function df['column'].astype(dtype) to ensure your data columns are in the correct format you need for your analysis.
   
6. 𝗮𝗽𝗽𝗹𝘆(): Apply custom functions to your data. df['column'].apply(func) lets you perform complex transformations and calculations. It works with both standard and lambda functions.
   
7. 𝘀𝘁𝗿.𝘀𝘁𝗿𝗶𝗽(): Clean up text data by removing leading and trailing whitespace. Using df['column'].str.strip() helps you to avoid hard-to-spot errors in string comparisons.
   
8. 𝘃𝗮𝗹𝘂𝗲_𝗰𝗼𝘂𝗻𝘁𝘀(): Get a quick summary of the frequency of values in a column. df['column'].value_counts() helps you understand the distribution of your data.
   
9. 𝗽𝗱.𝘁𝗼_𝗱𝗮𝘁𝗲𝘁𝗶𝗺𝗲(): Convert strings to datetime objects for accurate date and time manipulation. For time series analysis the use of pd.to_datetime(df['column']) will often be one of your first steps in data preparation.
   
10. 𝗴𝗿𝗼𝘂𝗽𝗯𝘆(): Aggregates data based on specific columns. Use df.groupby('column') to perform operations like sum, mean, or count on grouped data.

Learn to use these Python functions, to be able to transform a pile of messy data into the starting point of an impactful analysis.

Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1]

1. Data Cleaning and Preprocessing
   - Question: Can you walk me through the data cleaning process you followed in a Python-based project?
   - Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.
   - Tip: Mention specific functions you used, like dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method.

2. Exploratory Data Analysis (EDA)
   - Question: How did you perform EDA in a Python project? What tools did you use?
   - Answer: I used Pandas for data exploration, generating summary statistics with describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.
   - Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).

3. Pandas Operations
   - Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
   - Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.
   - Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like groupby(), merge(), concat(), or pivot().

4. Data Visualization
   - Question: How do you create visualizations in Python to communicate insights from data?
   - Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.
   - Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.me/sqlproject

ENJOY LEARNING 👍👍