Course Content
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Day-17: Complete EDA on Google PlayStore Apps
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Day-25: Quiz Time, Data Visualization-4
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Day-27: Data Scaling/Normalization/standardization and Encoding
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Day-30: NumPy (Part-3)
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Day-31: NumPy (Part-4)
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Day-32a: NumPy (Part-5)
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Day-32b: Data Preprocessing / Data Wrangling
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Day-37: Algebra in Data Science
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Day-56: Statistics for Data Science (Part-5)
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Day-69: Machine Learning (Part-3)
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Day-75: Machine Learning (Part-9)
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Day-81: Machine Learning (Part-15)-Evaluation Metrics
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Day-82: Machine Learning (Part-16)-Metrics for Classification
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Day-85: Machine Learning (Part-19)
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Day-89: Machine Learning (Part-23)
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Day-91: Machine Learning (Part-25)
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Day-93: Machine Learning (Part-27)
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Day-117: Deep Learning (Part-14)-Complete CNN Project
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Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)
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Day-121: Time Series Analysis (Part-1)
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Day-123: Time Series Analysis (Part-3)
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Day-128: Time Series Analysis (Part-8): Complete Project
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Day-129: git & GitHub Crash Course
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Day-131: Improving Machine/Deep Learning Model’s Performance
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Day-133: Transfer Learning and Pre-trained Models (Part-2)
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Day-134 Transfer Learning and Pre-trained Models (Part-3)
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Day-137: Generative AI (Part-3)
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Day-139: Generative AI (Part-5)-Tensorboard
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Day-145: Streamlit for webapp development and deployment (Part-1)
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Day-146: Streamlit for webapp development and deployment (Part-2)
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Day-147: Streamlit for webapp development and deployment (Part-3)
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Day-148: Streamlit for webapp development and deployment (Part-4)
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Day-149: Streamlit for webapp development and deployment (Part-5)
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Day-150: Streamlit for webapp development and deployment (Part-6)
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Day-151: Streamlit for webapp development and deployment (Part-7)
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Day-152: Streamlit for webapp development and deployment (Part-8)
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Day-153: Streamlit for webapp development and deployment (Part-9)
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Day-154: Streamlit for webapp development and deployment (Part-10)
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Day-155: Streamlit for webapp development and deployment (Part-11)
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Day-156: Streamlit for webapp development and deployment (Part-12)
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Day-157: Streamlit for webapp development and deployment (Part-13)
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How to Earn using Data Science and AI skills
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Day-160: Flask for web app development (Part-3)
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Day-161: Flask for web app development (Part-4)
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Day-162: Flask for web app development (Part-5)
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Day-163: Flask for web app development (Part-6)
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Day-164: Flask for web app development (Part-7)
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Day-165: Flask for web app deployment (Part-8)
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Day-167: FastAPI (Part-2)
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Day-168: FastAPI (Part-3)
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Day-169: FastAPI (Part-4)
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Day-170: FastAPI (Part-5)
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Day-171: FastAPI (Part-6)
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Day-174: FastAPI (Part-9)
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Six months of AI and Data Science Mentorship Program
About Lesson

Links to blogs:

  1. Importance of Doing EDA
  2. Missing values k Rolay
Join the conversation
Muhammad Uzair Madni 4 months ago
Here is my assignment link: https://colab.research.google.com/drive/1JUTEkTyliHUTXn8NvuuO9gqcPDuSOZ5F
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Muhammad Uzair Madni 4 months ago
Sir I Done with the code => def convert_to_mb(size): if size.endswith('k'): return str(round(float(size[:-1]) / 1024)) + 'M' else: return size df['Size'] = df['Size'].apply(convert_to_mb) df.head(10)it work 100%
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Muhammad Uzair Madni 4 months ago
I have read both blogs, but "Missing values ka rola" was very informative and attention-grabbing and warned me not to ignore missing values in any case.
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Abdullah Shahid 5 months ago
# Assuming you have a DataFrame df and a column 'size_MB' containing sizes in MB df['size_KB'] = df['size_MB'] * 1024 To convert Mbs to kbs in pyhton using pandas
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Hafiz Muhammad Gulzar Alam 5 months ago
import pandas as pd df = ... # your dataframe df['Size'] = df['Size'].str.lower() def convert_to_mb(size): if size[-1] == 'k': return f"{float(size[:-1]) * 1024/1024} Mb" else: return size df['Size'] = df['Size'].apply(convert_to_mb)
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Sheikh Hameed 6 months ago
import pandas as pddf = ... # your dataframedf['Size'] = df['Size'].str.lower()def convert_to_mb(size): if size[-1] == 'k': return f"{float(size[:-1]) * 1024/1024} Mb" else: return size df['Size'] = df['Size'].apply(convert_to_mb)
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Santosh Bhor 7 months ago
https://colab.research.google.com/drive/1AJ6tHeg7jCyJtt0TT6qa-ZZZQDgsVbeA?usp=sharing
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Almas Sultana 8 months ago
Removing $ sign from Price columndf['Price'] = pd.to_numeric(df['Price'].str.replace('[$,]', '', regex=True), errors='coerce').fillna(0)non numeric value are filled with 0
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Almas Sultana 8 months ago
df['Installs'] = df['Installs'].replace('[+,]', '', regex=True).astype(int)making binsbins = [0, 1000, 10000, 100000, 1000000, 10000000, 100000000, float('inf')] labels = ['0-1k', '1k-10k', '10k-100k', '100k-1M', '1M-10M', '10M-100M', '100M+']NEW Column with New Values df['Install Group'] = pd.cut(df['Installs'], bins=bins, labels=labels, right=False)
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Almas Sultana 8 months ago
df['Size'] = df['Size'].replace('Varies with device', '')
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