Full Exploratory Data Analysis (EDA) on House Prices Dataset

I have a dataset (House Prices) in .csv format, and I am looking for a data analyst who can perform a complete Exploratory Data Analysis (EDA). The goal is to clean the data, explore relationships, and generate insights that could later be useful for predictive modeling.

Tasks Required:

Data Cleaning & Preprocessing

Handle missing values

Remove duplicates

Standardize/encode categorical features where necessary

Basic feature engineering (date/time, length of text, etc. if applicable)

Data Summary

Dataset shape, column descriptions, data types

Descriptive statistics (mean, median, mode, variance, etc.)

Univariate Analysis

Distribution plots for numerical variables

Bar plots / frequency tables for categorical variables

Bivariate & Multivariate Analysis

Correlation heatmap of numerical features

Scatter plots / box plots for feature vs target variable

Multivariate visualization (optional: PCA, clustering)

Final Report / Summary

Key findings and insights

Well-structured Jupyter Notebook (or Colab) with both code and visualizations

A short summary (in text or markdown) highlighting major trends

Deliverables:

Jupyter Notebook (or Colab Notebook) with well-documented code

Visualizations (Matplotlib/Seaborn/Plotly)

Cleaned dataset (CSV)

Summary of analysis (Markdown or PDF report)

Preferred Skills:

Python (pandas, NumPy, matplotlib, seaborn, scikit-learn)

Strong knowledge of data cleaning and EDA

Ability to explain findings in a clear and structured way

Deadline:

5 days