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Data Science Projects and Deployment

What a real data science project looks like
• You start with a business problem
Example. Predict customer churn for a telecom company to reduce revenue loss.
• You define success metrics
Churn prediction accuracy above 80 percent. Recall more important than precision.
• You collect data
Sources include SQL databases, CSV files, APIs, logs. Typical size ranges from 50,000 rows to millions.
• You clean data
Remove duplicates. Handle missing values. Fix incorrect data types. 
Example. Convert dates, remove negative salaries.
• You explore data
Check distributions. Find correlations. Spot outliers. 
Example. Customers with low tenure churn more.
• You engineer features
Create new columns from raw data. 
Example. Average monthly spend, tenure buckets.
• You build models
Start simple. Logistic Regression, Decision Tree. Move to Random Forest, XGBoost if needed.
• You evaluate models
Use train test split or cross validation. Metrics depend on the problem. 
Classification. Accuracy, Precision, Recall, ROC AUC. 
Regression. RMSE, MAE.
• You select the final model
Balance performance and interpretability. 
Example. Slightly lower accuracy but easier to explain to stakeholders.

Common Real World Data Science Projects
• Sales forecasting
Predict next 3 to 6 months revenue using historical sales data.
• Customer churn prediction
Used by telecom, SaaS, OTT platforms.
• Recommendation systems
Products, movies, courses. Tech. Collaborative filtering, content based filtering.
• Fraud detection
Credit card transactions. Focus on recall. Missing fraud costs money.
• Sentiment analysis
Analyze reviews, tweets, feedback. Used in marketing and brand monitoring.
• Demand prediction
Used in e commerce and supply chain.

What Deployment Actually Means 
Deployment means your model runs automatically and gives predictions without you opening Jupyter Notebook. If your model is not deployed, it is not used.

Basic Deployment Options
• Batch prediction
Run the model daily or weekly. 
Example. Predict churn for all customers every night.
• Real time prediction
Prediction happens instantly via an API. 
Example. Fraud detection during a transaction.

Simple Deployment Workflow
• Save the trained model
Use pickle or joblib.
• Build an API
Use Flask or FastAPI.
• Load the model inside the API
The API takes input and returns predictions.
• Test locally
Send sample requests. Check responses.
• Deploy to cloud
AWS, GCP, Azure, Render, Railway.

Example Stack for Beginners
• Python
• Pandas, NumPy, Scikit learn
• Flask or FastAPI
• Docker
• AWS EC2 or Render

What MLOps Adds in Real Companies
• Model versioning
Track which model is in production.
• Data drift detection
Alert when incoming data changes.
• Model retraining
Automatically retrain with new data.
• Monitoring
Track accuracy, latency, failures.
• CI CD pipelines
Safe and repeatable deployments.

Tools Used in MLOps
• MLflow for experiments
• Docker for packaging
• Airflow for scheduling
• GitHub Actions for CI CD
• Prometheus and Grafana for monitoring

How You Should Present Projects in Your Resume
• Mention the business problem
• Mention dataset size
• Mention algorithms used
• Mention metrics achieved
• Mention deployment clearly
Example resume bullet: 
Built a customer churn prediction model on 200k records using Random Forest, achieved 84 percent recall, deployed as a REST API using FastAPI and Docker on AWS.

Common Mistakes to Avoid
• Only showing notebooks
• No clear business problem
• No metrics
• No deployment
• Using deep learning for small data without reason

Double Tap ♥️ For More

✅ Data Science Project Series: Part 1 - Loan Prediction.

Project goal
Predict loan approval using applicant data.

Business value
- Faster decisions
- Lower default risk
- Clear interview story

Dataset
Use the common Loan Prediction dataset from analytics practice platforms.

Target
Loan_Status
Y approved
N rejected

Tech stack
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn

Step 1. Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

Step 2. Load data

df = pd.read_csv("loan_prediction.csv")
df.head()

Step 3. Basic checks

df.shape
df.info()
df.isnull().sum()

Step 4. Data cleaning

Fill missing values

df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)
df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0], inplace=True)
df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)
categorical_cols = ['Gender','Married','Dependents','Self_Employed']
for col in categorical_cols:
    df[col].fillna(df[col].mode()[0], inplace=True)

Step 5. Exploratory Data Analysis

Credit history vs approval

sns.countplot(x='Credit_History', hue='Loan_Status', data=df)
plt.show()
Income distribution.python
sns.histplot(df['ApplicantIncome'], kde=True)
plt.show()

Insight
Applicants with credit history have far higher approval rates.

Step 6. Feature engineering
Create total income.

df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']

# Log transform loan amount
df['LoanAmount_log'] = np.log(df['LoanAmount'])

Step 7. Encode categorical variables

le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])

Step 8. Split features and target

X = df.drop('Loan_Status', axis=1)
y = df['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

Step 9. Build model
Logistic Regression.

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

Step 10. Predictions

y_pred = model.predict(X_test)

Step 11. Evaluation

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
confusion_matrix(y_test, y_pred)
Classification report.python
print(classification_report(y_test, y_pred))

Typical result
- Accuracy around 80 percent
- Strong precision for approved loans
- Recall needs focus for rejected loans

Step 12. Model improvement ideas
- Use Random Forest
- Tune hyperparameters
- Handle class imbalance
- Track recall for rejected cases

Resume bullet example
- Built loan approval prediction model using Logistic Regression
- Achieved ~80 percent accuracy
- Identified credit history as top approval driver

Interview explanation flow
- Start with bank risk problem
- Explain feature impact
- Justify Logistic Regression
- Discuss recall vs accuracy

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✅ Data Science Project Series Part-2: Customer Churn Prediction

Project goal
Predict which customers will leave. Act before revenue drops.

Business value
• Retention costs less than acquisition
• Clear actions for sales and support
• High interview relevance

Dataset
Telco customer churn style dataset.
Target: Churn (Yes left, No stayed)

Key features
• tenure
• MonthlyCharges
• TotalCharges
• Contract
• PaymentMethod
• InternetService

Tech stack
• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Step 1. Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score

Step 2. Load data

df = pd.read_csv("customer_churn.csv")
df.head()

Step 3. Basic checks

df.shape
df.info()
df.isnull().sum()

Step 4. Data cleaning
Convert TotalCharges to numeric.

df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
df['TotalCharges'].fillna(df['TotalCharges'].median(), inplace=True)

Drop customer ID.

df.drop('customerID', axis=1, inplace=True)

Step 5. Exploratory Data Analysis
Churn distribution.

sns.countplot(x='Churn', data=df)
plt.show()

Tenure vs churn.

sns.boxplot(x='Churn', y='tenure', data=df)
plt.show()

Common insights:
• Month-to-month contracts churn more
• Low tenure users churn early
• High monthly charges increase churn

Step 6. Encode categorical variables

le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])

Step 7. Feature scaling

scaler = StandardScaler()
num_cols = ['tenure', 'MonthlyCharges', 'TotalCharges']
df[num_cols] = scaler.fit_transform(df[num_cols])

Step 8. Split data

X = df.drop('Churn', axis=1)
y = df['Churn']
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y
)

Step 9. Build model

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

Step 10. Predictions

y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:,1]

Step 11. Evaluation

confusion_matrix(y_test, y_pred)
print(classification_report(y_test, y_pred))
roc_auc_score(y_test, y_prob)

Typical results:
• Accuracy around 78 to 83 percent
• ROC AUC around 0.84
• Recall for churn is key metric

Step 12. Business actions from model
• Target high-risk users
• Offer discounts to month-to-month users
• Push yearly contracts
• Improve onboarding for first 90 days

Resume bullet example:
• Built churn prediction model using Logistic Regression
• Identified contract type and tenure as top churn drivers
• Improved churn recall using class-aware split

Interview explanation flow:
• Revenue loss problem
• Why recall matters more than accuracy
• How features map to actions

Mini task for you:
• Train Random Forest
• Compare ROC AUC
• Tune threshold for higher recall

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✅ Data Science Project Series: Part 3 - Credit Card Fraud Detection.

Project goal
Detect fraudulent credit card transactions.

Why this project matters
- High financial risk
- Strong interview signal
- Shows imbalanced data handling
- Focus on recall over accuracy

Business problem
Fraud cases are rare. Missing fraud costs money. False alarms hurt customers. You balance both.

Dataset
Credit card transactions dataset. Target Class 0 genuine 1 fraud

Data reality
- Fraud less than 1 percent
- Accuracy becomes misleading

Tech stack
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn

Step 1. Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score

Step 2. Load data

df = pd.read_csv("creditcard.csv")
df.head()

Step 3. Basic checks

df.shape
df['Class'].value_counts()

Output example:
• Genuine 284315
• Fraud 492

Step 4. Data understanding

Check class imbalance:

sns.countplot(x='Class', data=df)
plt.show()

Insight Highly imbalanced dataset.

Step 5. Feature scaling

Scale Amount column:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df['Amount'] = scaler.fit_transform(df[['Amount']])
Drop Time.python
df.drop('Time', axis=1, inplace=True)

Step 6. Split features and target

X = df.drop('Class', axis=1)
y = df['Class']
X_train, X_test, y_train, y_test = train_test_split(
  X, y, test_size=0.3, random_state=42, stratify=y
)

Step 7. Baseline model

Logistic Regression with class weight:

model = LogisticRegression(
  max_iter=1000, class_weight='balanced'
)
model.fit(X_train, y_train)

Why class_weight
• Penalizes fraud mistakes more
• Improves recall

Step 8. Predictions

y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:,1]

Step 9. Evaluation

Confusion matrix:

confusion_matrix(y_test, y_pred)

Classification report:

print(classification_report(y_test, y_pred))

ROC AUC:

roc_auc_score(y_test, y_prob)

Typical results
• Accuracy looks high but ignored
• Fraud recall improves sharply
• ROC AUC around 0.97

Step 10. Threshold tuning

Increase fraud recall:

y_pred_custom = (y_prob > 0.3).astype(int)
confusion_matrix(y_test, y_pred_custom)

Business logic Lower threshold catches more fraud. More false alerts accepted.

Step 11. Advanced approach

Random Forest:

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(
  n_estimators=100, class_weight='balanced', random_state=42
)
rf.fit(X_train, y_train)
rf_prob = rf.predict_proba(X_test)[:,1]
roc_auc_score(y_test, rf_prob)

Resume bullet example
- Built fraud detection model on highly imbalanced data
- Improved fraud recall using class weighting and threshold tuning
- Evaluated model using ROC AUC instead of accuracy

Interview explanation flow
- Explain imbalance problem
- Why accuracy fails
- Why recall matters
- How threshold changes business impact

Mini task for you
- Apply SMOTE
- Compare with Isolation Forest
- Plot Precision Recall curve

Double Tap ♥️ For More

✅ Data Science Project Series Part 4: Sales Forecasting using Time Series.

Project Goal
Predict future sales using historical data.

Business Value
- Inventory planning
- Revenue forecasting
- Staffing decisions
- Strong analytics interview case

Dataset
Monthly or daily sales data. Typical columns:
- Date
- Sales
Target: Future sales values.

Key Concept
Time order matters. No random shuffling.

Tech Stack
- Python
- Pandas
- NumPy
- Matplotlib
- Statsmodels
- Scikit-learn

Step 1. Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_absolute_error, mean_squared_error

Step 2. Load Data

df = pd.read_csv("sales.csv")
df.head()

Step 3. Date Handling

df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
# Sort by date
df = df.sort_index()

Step 4. Visualize Sales Trend

plt.plot(df.index, df['Sales'])
plt.noscript("Sales over time")
plt.show()

What you observe:
- Trend
- Seasonality
- Sudden spikes

Step 5. Decompose Time Series

decomposition = seasonal_decompose(df['Sales'], model='additive')
decomposition.plot()
plt.show()

Insight
- Trend shows long-term growth
- Seasonality repeats yearly or monthly

Step 6. Train Test Split
Split by time.

train = df.iloc[:-12]
test = df.iloc[-12:]

Why Last 12 months simulate future.

Step 7. Build ARIMA Model

model = ARIMA(train['Sales'], order=(1,1,1))
model_fit = model.fit() # corrected from (link unavailable)

Order meaning
- p: autoregressive
- d: differencing
- q: moving average

Step 8. Forecast

forecast = model_fit.forecast(steps=12)
print(forecast)

Step 9. Plot Forecast vs Actual

plt.plot(train.index, train['Sales'], label='Train')
plt.plot(test.index, test['Sales'], label='Actual')
plt.plot(test.index, forecast, label='Forecast')
plt.legend()
plt.show()

Step 10. Evaluation

mae = mean_absolute_error(test['Sales'], forecast)
rmse = np.sqrt(mean_squared_error(test['Sales'], forecast))
print("MAE:", mae)
print("RMSE:", rmse)

Typical results:
- RMSE depends on scale
- Trend captured well
- Peaks harder to predict

Step 11. Business Interpretation
- Underforecast leads to stockouts
- Overforecast leads to inventory waste
- Accuracy matters near peaks

Model Improvement Ideas
- SARIMA for seasonality
- Prophet for business calendars
- Add promotions and holidays

Resume Bullet Example
- Built time series model to forecast monthly sales
- Used ARIMA with rolling time-based split
- Reduced forecasting error using trend analysis

Interview Explanation Flow
- Why random split fails
- Importance of seasonality
- Error metrics selection

Mini Task for You
- Try SARIMA
- Forecast next 24 months
- Compare RMSE across models

Double Tap ♥️ For More

Data Science Project Series Part 5: Recommendation System ✅

Project goal
Recommend items users are likely to like.

Business value
• Higher engagement
• Higher sales
• Strong ML interview topic

Use cases
• Movies
• Products
• Courses
• Videos

Dataset
User item ratings. Typical columns
• user_id
• item_id
• rating

Approach used
Collaborative filtering. User based similarity.

Step 1. Import libraries

import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

Step 2. Load data

df = pd.read_csv("ratings.csv")
df.head()

Example data
user_id | item_id | rating
1 | 101 | 5
1 | 102 | 3

Step 3. Create user item matrix

user_item_matrix = df.pivot_table(
    index='user_id',
    columns='item_id',
    values='rating'
)

Matrix shape
Rows users
Columns items
Values ratings

Step 4. Handle missing values

user_item_matrix.fillna(0, inplace=True)

Why? Cosine similarity needs numbers.

Step 5. Compute user similarity

user_similarity = cosine_similarity(user_item_matrix)
user_similarity_df = pd.DataFrame(
    user_similarity,
    index=user_item_matrix.index,
    columns=user_item_matrix.index
)

Step 6. Find similar users

user_id = 1

similar_users = user_similarity_df[user_id].sort_values(ascending=False)
similar_users.head()

Top result User itself score 1. Ignore it.

Step 7. Recommend items

Get items rated by similar users

similar_users = similar_users[similar_users.index != user_id]
weighted_ratings = user_item_matrix.loc[similar_users.index].T.dot(similar_users)
recommendations = weighted_ratings.sort_values(ascending=False)

Remove already rated items.

already_rated = user_item_matrix.loc[user_id]
already_rated = already_rated[already_rated > 0].index
recommendations = recommendations.drop(already_rated)
recommendations.head(5)

Output Top 5 recommended item IDs.

Step 8. Why cosine similarity
• Focuses on rating pattern
• Ignores scale differences
• Fast and simple

Limitations
• Cold start problem
• Sparse matrix
• No item features

Improvements
• Item based filtering
• Matrix factorization
• Hybrid models

Resume bullet example
• Built recommendation system using collaborative filtering
• Used cosine similarity on user item matrix
• Generated personalized item recommendations

Interview explanation flow
• Difference between content based and collaborative
• Why sparsity hurts
• Cold start solutions

Mini task for you
• Convert to item based filtering
• Add minimum similarity threshold
• Evaluate using precision at K

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Data Science Project Series Part 6: Sentiment Analysis using NLP ✅

Project Goal
Classify text as positive or negative.

Business Value
• Track customer feedback
• Monitor brand sentiment
• Automate review analysis
• High NLP interview relevance

Dataset
Movie reviews or product reviews.
Typical columns:
• review
• sentiment
Target: sentiment (1 positive, 0 negative)

Tech Stack
• Python
• Pandas
• NumPy
• NLTK
• Scikit-learn

Step 1. Import libraries

import pandas as pd
import numpy as np
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

nltk.download('stopwords')

Step 2. Load data

df = pd.read_csv("sentiment.csv")
df.head()

Example review: "The movie was amazing" sentiment: 1

Step 3. Basic checks

df.shape
df['sentiment'].value_counts()

Step 4. Text cleaning

stemmer = PorterStemmer()
stop_words = set(stopwords.words('english'))

def clean_text(text):
    text = text.lower()
    text = re.sub('[^a-z]', ' ', text)
    words = text.split()
    words = [stemmer.stem(w) for w in words if w not in stop_words]
    return ' '.join(words)

df['clean_review'] = df['review'].apply(clean_text)

Step 5. Train test split

X = df['clean_review']
y = df['sentiment']
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y
)

Step 6. Text vectorization TF IDF

tfidf = TfidfVectorizer(max_features=5000)
X_train_tfidf = tfidf.fit_transform(X_train)
X_test_tfidf = tfidf.transform(X_test)

Why TF IDF
• Reduces common word weight
• Keeps meaningful words

Step 7. Model building

model = LogisticRegression(max_iter=1000)
model.fit(X_train_tfidf, y_train)

Step 8. Predictions

y_pred = model.predict(X_test_tfidf)

Step 9. Evaluation

accuracy_score(y_test, y_pred)
confusion_matrix(y_test, y_pred)
print(classification_report(y_test, y_pred))

Typical results
• Accuracy 85 to 90 percent
• Precision strong on positive reviews
• Neutral text harder to classify

Step 10. Test on custom text

sample = ["The product quality is terrible"]
sample_clean = [clean_text(sample[0])]
sample_vec = tfidf.transform(sample_clean)
model.predict(sample_vec)

Output: 0 negative

Common interview questions

• Why TF IDF over CountVectorizer
• How stopwords affect meaning
• Why Logistic Regression works well

Improvements
• Use n grams
• Try Naive Bayes
• Use LSTM or Transformers

Resume bullet example

• Built sentiment analysis model using TF IDF and Logistic Regression
• Achieved 88 percent accuracy on review data
• Automated text preprocessing pipeline

Mini task for you
• Add bigrams
• Compare Naive Bayes
• Plot ROC curve

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Data Science Project Series Part 7: House Price Prediction ✅

Project goal
Predict house prices using property features.

Business value
• Real estate valuation
• Investment decisions
• Pricing strategy
• Classic regression interview problem

Dataset
Housing data. Typical columns
• area
• bedrooms
• bathrooms
• location
• parking
• price
Target price.

Tech stack
• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Step 1. Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

Step 2. Load data

df = pd.read_csv("house_prices.csv")
df.head()

Step 3. Basic checks

df.shape
df.info()
df.isnull().sum()

Step 4. Data cleaning
Fill missing values.

df.fillna(df.median(numeric_only=True), inplace=True)

Step 5. Encode categorical variables

le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])

Step 6. Feature scaling

scaler = StandardScaler()
X = df.drop('price', axis=1)
y = df['price']
X_scaled = scaler.fit_transform(X)

Step 7. Train test split

X_train, X_test, y_train, y_test = train_test_split(
    X_scaled, y, test_size=0.3, random_state=42
)

Step 8. Build model
Linear Regression.

model = LinearRegression()
model.fit(X_train, y_train)

Step 9. Predictions

y_pred = model.predict(X_test)

Step 10. Evaluation

mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print("MAE:", mae)
print("RMSE:", rmse)
print("R2:", r2)

Typical results
• R2 between 0.70 to 0.85
• Location and area dominate price

Step 11. Feature importance

importance = pd.DataFrame({
    'Feature': X.columns,
    'Coefficient': model.coef_
}).sort_values(by='Coefficient', ascending=False)
importance

Interpretation: Positive coefficient increases price. Negative reduces price.

Step 12. Model improvements
• Ridge regression for multicollinearity
• Lasso for feature selection
• Random Forest for non-linear patterns

Resume bullet example
• Built house price prediction model using regression
• Achieved R2 score above 0.8
• Identified key price drivers

Interview explanation flow
• Why RMSE matters
• How multicollinearity affects coefficients
• Why tree models outperform linear sometimes

Mini task for you
• Try Ridge and Lasso
• Compare RMSE
• Plot actual vs predicted

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