Experiment Template¶

Experiment Information¶

  • Experiment Name: [Your experiment name here]
  • Date Started: [Today's date]
  • Topics Applied: [Which topics from 01-09 are you using?]
  • Estimated Time: [How long do you think this will take?]

Hypothesis¶

What do you expect to happen?

[Write your hypothesis here - what you think will work and why]

Research Question¶

What specific question are you trying to answer?

[Write your research question here]

Background & Motivation¶

Why is this experiment important?

[Explain the motivation and background for this experiment]

Setup & Imports¶

In [ ]:
# Standard imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# NLP specific imports
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report

# Add other imports as needed
# import tensorflow as tf
# from transformers import ...

# Set random seeds for reproducibility
np.random.seed(42)

# Configure display options
plt.style.use('default')
sns.set_palette("husl")
%matplotlib inline

print("Setup complete!")

Data Loading & Exploration¶

Describe your data:

  • Source: [Where is your data coming from?]
  • Size: [How much data do you have?]
  • Format: [What format is your data in?]
In [ ]:
# Load your data here
# data = pd.read_csv('your_data.csv')
# OR create sample data for testing

# Sample data for demonstration
sample_texts = [
    "This is a positive example of text.",
    "This is a negative example of text.",
    "Another positive sample for testing.",
    "Another negative sample for testing."
]

sample_labels = [1, 0, 1, 0]  # 1 for positive, 0 for negative

print(f"Number of samples: {len(sample_texts)}")
print(f"Sample text: {sample_texts[0]}")
In [ ]:
# Basic data exploration
# Add your exploration code here
# - Text length distributions
# - Label distributions
# - Sample visualizations

# Example exploration
text_lengths = [len(text.split()) for text in sample_texts]
print(f"Average text length: {np.mean(text_lengths):.2f} words")
print(f"Text length range: {min(text_lengths)} - {max(text_lengths)} words")

Methodology¶

Describe your approach:

  1. [Step 1 of your methodology]
  2. [Step 2 of your methodology]
  3. [Step 3 of your methodology]

Techniques you'll use:

  • [Technique 1]
  • [Technique 2]
  • [Technique 3]

Implementation¶

In [ ]:
# Preprocessing steps
def preprocess_text(text):
    """
    Add your text preprocessing steps here
    """
    # Example preprocessing
    text = text.lower()
    # Add more preprocessing as needed
    return text

# Apply preprocessing
processed_texts = [preprocess_text(text) for text in sample_texts]
print(f"Processed example: {processed_texts[0]}")
In [ ]:
# Feature extraction
# Add your feature extraction code here
# Examples:
# - TF-IDF vectorization
# - Word embeddings
# - Custom features

# Example TF-IDF
vectorizer = TfidfVectorizer(max_features=1000)
# X = vectorizer.fit_transform(processed_texts)
# print(f"Feature matrix shape: {X.shape}")
In [ ]:
# Model implementation
# Add your model code here
# Examples:
# - Classification models
# - Neural networks
# - Custom algorithms

print("Model implementation goes here...")
In [ ]:
# Training/fitting your model
# Add training code here

print("Model training goes here...")

Results & Analysis¶

In [ ]:
# Evaluation metrics
# Add your evaluation code here
# - Accuracy, precision, recall, F1
# - Confusion matrices
# - Custom metrics

print("Evaluation results will appear here...")
In [ ]:
# Visualizations
# Add your visualization code here
# - Performance plots
# - Learning curves
# - Feature importance
# - Error analysis

plt.figure(figsize=(10, 6))
# Your plots here
plt.title("Results Visualization")
plt.show()

Discussion¶

Key Findings¶

  1. [Finding 1]
  2. [Finding 2]
  3. [Finding 3]

Hypothesis Validation¶

Was your hypothesis correct? [Discuss whether your hypothesis was supported by the results]

Unexpected Results¶

[Describe any surprising or unexpected findings]

Limitations¶

  • [Limitation 1]
  • [Limitation 2]
  • [Limitation 3]

Conclusions & Next Steps¶

Main Conclusions¶

  1. [Conclusion 1]
  2. [Conclusion 2]
  3. [Conclusion 3]

Lessons Learned¶

  • [Lesson 1]
  • [Lesson 2]
  • [Lesson 3]

Future Work¶

  • [Next experiment idea 1]
  • [Next experiment idea 2]
  • [Next experiment idea 3]

Applications¶

How could this be used in practice? [Describe potential real-world applications]

Skills Developed¶

What new skills did you learn?

  • [Skill 1]
  • [Skill 2]
  • [Skill 3]

Experiment Log¶

Time Tracking¶

  • Planning Time: [X hours]
  • Implementation Time: [X hours]
  • Analysis Time: [X hours]
  • Documentation Time: [X hours]
  • Total Time: [X hours]

Challenges Faced¶

  1. [Challenge 1 and how you solved it]
  2. [Challenge 2 and how you solved it]
  3. [Challenge 3 and how you solved it]

Resources Used¶

  • [Paper/Tutorial 1]
  • [Paper/Tutorial 2]
  • [Code repository/library]

Code Quality Notes¶

  • Code is well-commented
  • Functions are properly documented
  • Results are reproducible
  • Code follows best practices

Experiment Status: [In Progress / Completed / Paused]

Next Review Date: [When will you review this experiment?]

Share with Community: [Yes/No - Is this worth sharing?]