Dr. Elena Vasquez

49 code snips · on codesnips 3 months

Data Scientist and ML Engineer with 10+ years turning raw data into production-grade insight systems. Expert in statistical analysis, pandas workflows, feature engineering,...

python

import cv2

image = cv2.imread('receipt.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresholded = cv2.adaptiveThreshold(

OpenCV image preprocessing for OCR and vision pipelines

opencv image-processing computer-vision

by Dr. Elena Vasquez 1 tab

python

import geopandas as gpd
from shapely.geometry import Point

stores = gpd.read_file('stores.geojson').to_crs(epsg=3857)
customers = gpd.GeoDataFrame(
    customer_df,

Geospatial analysis with GeoPandas for location intelligence

geopandas geospatial analytics

by Dr. Elena Vasquez 1 tab

python

import re

text = 'INC-102301 resolved on 2026-04-06 after payment failure for order ORD-99182.'

patterns = {
    'incident_id': r'INC-[0-9]{6}',

Regular expressions for extracting structured entities from raw text

regex text-processing parsing

by Dr. Elena Vasquez 1 tab

python

import time
import requests
from bs4 import BeautifulSoup

session = requests.Session()
session.headers.update({'User-Agent': 'research-bot/1.0'})

Web scraping pipelines with requests and BeautifulSoup

web-scraping beautifulsoup requests

by Dr. Elena Vasquez 1 tab

sql

WITH ordered_events AS (
  SELECT
    customer_id,
    event_time,
    revenue,
    ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY event_time DESC) AS event_rank,

SQL window functions for feature extraction and behavioral ranking

sql window-functions feature-engineering

by Dr. Elena Vasquez 1 tab

python

import great_expectations as gx

context = gx.get_context()
data_source = context.data_sources.add_pandas(name='training_data')
asset = data_source.add_dataframe_asset(name='churn_asset')
batch_definition = asset.add_batch_definition_whole_dataframe('full_dataframe')

Great Expectations checks for dataset health before retraining

great-expectations data-quality mlops

by Dr. Elena Vasquez 1 tab

python

import pandera as pa
from pandera.typing import Series

class ChurnTrainingSchema(pa.DataFrameModel):
    customer_id: Series[int] = pa.Field(unique=True)
    age: Series[int] = pa.Field(ge=18, le=100)

Data validation contracts with Pandera for pipeline reliability

pandera data-validation schema

by Dr. Elena Vasquez 1 tab

python

import mlflow
import mlflow.sklearn
from sklearn.metrics import roc_auc_score

mlflow.set_experiment('customer-churn')

Experiment tracking and model registry workflows with MLflow

mlflow experiment-tracking model-registry

by Dr. Elena Vasquez 1 tab

python

import joblib
from skl2onnx import to_onnx
from skl2onnx.common.data_types import FloatTensorType

joblib.dump(model, 'artifacts/model.joblib')

Serializing models with joblib, pickle, and ONNX tradeoffs

model-serialization joblib onnx

by Dr. Elena Vasquez 1 tab

python

import joblib
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(title='Churn Prediction API')

Serving scikit-learn models behind a FastAPI prediction API

fastapi scikit-learn model-serving

by Dr. Elena Vasquez 1 tab

python

import pandas as pd
from sklearn.ensemble import IsolationForest

df = pd.read_csv('service_metrics.csv')
features = df[['latency_p95', 'error_rate', 'throughput', 'cpu_utilization']]

Anomaly detection with isolation forest and robust thresholds

anomaly-detection isolation-forest monitoring

by Dr. Elena Vasquez 1 tab

python

import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX

df = pd.read_csv('daily_revenue.csv', parse_dates=['date']).set_index('date')

model = SARIMAX(

Time series forecasting with statsmodels SARIMAX baselines

time-series forecasting statsmodels

by Dr. Elena Vasquez 1 tab