Stanford's "Design and Analysis of Algorithms" Winter 2025
Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/
Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/
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"Introduction to Probability for Data Science"
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
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@CodeProgrammer Matplotlib.pdf
4.3 MB
The Complete Visual Guide for Data Enthusiasts
Matplotlib is a powerful Python library for data visualization, essential not only for acing job interviews but also for building a solid foundation in analytical thinking and data storytelling.
This step-by-step tutorial guide walks learners through everything from the basics to advanced techniques in Matplotlib. It also includes a curated collection of the most frequently asked Matplotlib-related interview questions, making it an ideal resource for both beginners and experienced professionals.
#Matplotlib #DataVisualization #Python #DataScience #InterviewPrep #Analytics #TechCareer #LearnToCode
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A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly:
🙂 Positive sentiment
☹️ Negative sentiment
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
🔗 GitHub: https://lnkd.in/e_gk3hfe
📰 Article: https://lnkd.in/e_baNJd2
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
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Python Cheat Sheet
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#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath
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After counting the vehicles, we need to visualize the results on the video frame. We will draw the lane polygons, display the vehicle count for each, and change the lane's color to red if it is considered congested based on our threshold.
---
When you run the script, a video window will appear. You will see:
• Yellow polygons outlining the defined lanes.
• Text at the top indicating the number of vehicles in each lane and its status ("NORMAL" or "CONGESTED").
• The status text and its color will change in real-time based on the vehicle count exceeding the
Discussion of Results:
Threshold is Key: The
Polygon Accuracy: The system's accuracy is highly dependent on how well you define the
Limitations: This method only measures vehicle density (number of cars in an area). It does not measure traffic flow (vehicle speed). A lane could have many cars moving quickly (high density, but not congested) or a few stopped cars (low density, but very congested).
Potential Improvements:
Object Tracking: Implement an object tracker (like DeepSORT or BoT-SORT) to assign a unique ID to each car. This would allow you to calculate the average speed of vehicles within each lane, providing a much more reliable measure of congestion.
Time-Based Analysis: Analyze data over time. A lane that is consistently above the threshold for more than a minute is a stronger indicator of a traffic jam than a brief spike in vehicle count.
#ProjectComplete #AIforCities #Transportation
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By: @CodeProgrammer ✨
# --- Visualization --- (This code continues inside the while loop)
# Draw the lane polygons on the frame
cv2.polylines(frame, [LANE_1_POLYGON], isClosed=True, color=(255, 255, 0), thickness=2)
cv2.polylines(frame, [LANE_2_POLYGON], isClosed=True, color=(255, 255, 0), thickness=2)
# Check for congestion and display status for Lane 1
if lane_1_count > CONGESTION_THRESHOLD:
status_1 = "CONGESTED"
color_1 = (0, 0, 255) # Red
else:
status_1 = "NORMAL"
color_1 = (0, 255, 0) # Green
cv2.putText(frame, f"Lane 1: {lane_1_count} ({status_1})", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color_1, 2)
# Check for congestion and display status for Lane 2
if lane_2_count > CONGESTION_THRESHOLD:
status_2 = "CONGESTED"
color_2 = (0, 0, 255) # Red
else:
status_2 = "NORMAL"
color_2 = (0, 255, 0) # Green
cv2.putText(frame, f"Lane 2: {lane_2_count} ({status_2})", (530, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color_2, 2)
# Display the frame with detections and status
cv2.imshow("Traffic Congestion Monitor", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
# Hashtags: #DataVisualization #OpenCV #TrafficFlow
---
#Step 5: Results and DiscussionWhen you run the script, a video window will appear. You will see:
• Yellow polygons outlining the defined lanes.
• Text at the top indicating the number of vehicles in each lane and its status ("NORMAL" or "CONGESTED").
• The status text and its color will change in real-time based on the vehicle count exceeding the
CONGESTION_THRESHOLD.Discussion of Results:
Threshold is Key: The
CONGESTION_THRESHOLD is the most important variable to tune. A value of 10 might be too high for a short lane or too low for a long one. It must be calibrated based on the specific camera view and what is considered "congested" for that road.Polygon Accuracy: The system's accuracy is highly dependent on how well you define the
LANE_POLYGON coordinates. They must accurately map to the lanes in the video, accounting for perspective.Limitations: This method only measures vehicle density (number of cars in an area). It does not measure traffic flow (vehicle speed). A lane could have many cars moving quickly (high density, but not congested) or a few stopped cars (low density, but very congested).
Potential Improvements:
Object Tracking: Implement an object tracker (like DeepSORT or BoT-SORT) to assign a unique ID to each car. This would allow you to calculate the average speed of vehicles within each lane, providing a much more reliable measure of congestion.
Time-Based Analysis: Analyze data over time. A lane that is consistently above the threshold for more than a minute is a stronger indicator of a traffic jam than a brief spike in vehicle count.
#ProjectComplete #AIforCities #Transportation
━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨
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