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 \u003cp align=\"center\"\u003e [Specialized Consulting for Integrated Project: Data Mining]()\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n#### \u003cp align=\"center\"\u003e [![Sponsor Quantum Software Development](https://img.shields.io/badge/Sponsor-Quantum%20Software%20Development-brightgreen?logo=GitHub)](https://github.com/sponsors/Quantum-Software-Development)\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/user-attachments/assets/0d6324da-9468-455e-b8d1-2cce8bb63b06\" /\u003e\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n[**Institution:**]() Pontifical Catholic University of São Paulo (PUC-SP)  \n[**School:**]() Faculty of Interdisciplinary Studies  \n[**Program:**]() Humanistic AI and Data Science\n[**Semester:**]() 2nd Semester 2025  \nProfessor:  [***Professor Doctor in Mathematics Daniel Rodrigues da Silva***](https://www.linkedin.com/in/daniel-rodrigues-048654a5/)\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n## ⚠️ [Important Notes]()\n\n\n- [Whenever possible](), projects and deliverables developed during the course will be made [publicly accessible]().\n\n- The course emphasizes [**practical, hands-on experience**]() with real datasets to emulate professional consulting scenarios in the field of Data Mining.\n\n- All activities and materials will strictly adhere to the [**academic and ethical guidelines of PUC-SP**](). Any content not authorized for public disclosure will remain [**confidential**]() and stored in [private repositories]().\n\n\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n## Table of Contents\n\n\u003cbr\u003e\n\n\n1. [Course Overview](#course-overview)\n   - I - [class 1 - Intoductioon and Assessment](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/a98512aa9dc2525446a3ffb236d06cbfb16d1f43/class_1-Introduction)\n   - II - [class_2 - Introduction - Data Mining With Python](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/a98512aa9dc2525446a3ffb236d06cbfb16d1f43/class_2%20-%20Introduction%20-%20Data%20Mining%20With%20Python)\n   - III - [class_3 - Stats Review](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/a98512aa9dc2525446a3ffb236d06cbfb16d1f43/class_3%20-%20Stats%20Review)\n2. [Objectives](#objectives)\n3. [Syllabus](#syllabus)\n4. [Weekly Schedule](#weekly-schedule)\n5. [Tools and Technologies](#tools-and-technologies)\n6. [Installation and Setup](#installation-and-setup)\n7. [Assessment](#assessment)\n8. [Bibliography](#bibliography)\n   - [Basic Bibliography](#basic-bibliography)\n   - [Complementary Bibliography](#complementary-bibliography)\n9. [Notes](#notes)\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n##  [Course Overview]()\n\n\u003cbr\u003e\n\n\nThis course introduces [**data mining techniques**]() with a focus on [**unsupervised learning methods**](), including:\n\n- Clustering algorithms (K-Means, Affinity Propagation, Mean-Shift)\n- Principal Component Analysis (PCA)\n- Dictionary Learning\n- Novelty and outlier detection\n\nStudents will work on [**practical projects**]() inspired by real-world problem-solving in third-sector organizations. Final deliverables will be shared in **open repositories** and made available to the broader community, schools, libraries, and non-profits.\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n## [Objectives]()\n\nEnable students to **plan, conduct, and complete a research project** applying key **data mining concepts, algorithms, and methodologies**.\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n## [Syllabus]()\n\n\u003cbr\u003e\n\n- Fundamentals of Data Mining\n- Data cleaning and preparation\n- Predictive analysis\n- Clustering methods (K-Means, Affinity Propagation, Mean-Shift)\n- Principal Component Analysis (PCA)\n- Dictionary Learning\n- Novelty and outlier detection\n- Application of concepts to real-world consulting scenarios\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n##  [Weekly Schedule]()\n\n\u003cbr\u003e\n\n| [Week]() | [Repos]() | [Methodology]() | [Tools]() |\n|------|-------|-------------|-------|\n| 1    | [Course introduction](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/d737ff164c6b4d6e580d5ba6e95c54ac604f7ea4/class_1-Introduction) | Active methodology | – |\n| 2–3 | [Review of statistical methods](https://github.com/Quantum-Software-Development/class_2-and-3-intro-data-mining-python) | Active methodology | Python |\n| 4 | Fundamentals of Data Mining | Active methodology | Python |\n| 5–6 | Data cleaning and preparation | Active methodology | Python |\n| 7 | Predictive analysis | Active methodology | Python |\n| 8, 10 | Clustering techniques | Active methodology | Python |\n| 9 | **P1 Exam** | Written (Individual) | – |\n| 11 | K-Means algorithm | Active methodology | Python |\n| 12 | Affinity Propagation | Active methodology | Python |\n| 13 | Mean-Shift algorithm | Active methodology | Python |\n| 14 | Principal Component Analysis (PCA) | Active methodology | Python |\n| 15 | Dictionary Learning | Active methodology | Python |\n| 16 | **P2 Exam** | Written (Individual) | – |\n| 17 | **P3 Exam \u0026 Grade Closure** | Written (Individual) | – |\n| 18 | Final grade submission | – | – |\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n##  [Tools and Technologies]()\n\n\u003cbr\u003e\n\n- **Programming Language:** Python  \n- **Libraries:** NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn  \n- **Environment:** Jupyter Notebook or other Python IDEs\n\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n\n##  Installation and Setup\n\n\u003cbr\u003e\n\nFollow these steps to set up your local environment for the course projects:\n\n\u003cbr\u003e\n\n[1](). **Clone the repository**\n\n\n```\ngit clone https://github.com/\u003cusername\u003e/\u003crepository-name\u003e.git\ncd \u003crepository-name\u003e\n```\n\n\n\u003cbr\u003e\n\n\n[2](). **Create a virtual environment** (recommended)\n\n\n```\npython -m venv venv\nsource venv/bin/activate   \\# Mac/Linux\nvenv\\Scripts\\activate      \\# Windows\n```\n\n\n\u003cbr\u003e\n\n\n[3](). **Install dependencies**\nMake sure `pip` is updated:\n```\n\npip install --upgrade pip\n\n```\nThen install the required packages:\n```\n\npip install -r requirements.txt\n\n```\n*(If `requirements.txt` is not provided, install manually:)*  \n```\n\npip install numpy pandas scikit-learn matplotlib seaborn jupyter\n```\n\n\n\u003cbr\u003e\n\n\n[4](). **Run Jupyter Notebook**\n   \n```\njupyter notebook\n```\n\n\n\u003cbr\u003e\n\n\n[5](). **Open course notebooks** and start practicing.\n\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n#  I - [Intoductioon and Assessment](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/86d9d9fbc56efdd0b8e377955c1c7abf8879b775/class_1-Introduction)\n\n\u003cbr\u003e\n\n\n| Exam | Date | Format | Weight |\n|------|------|--------|--------|\n| **P1** | 01/10/2025 | Written – Individual | Arithmetic mean |\n| **P2** | 19/11/2025 | Written – Individual | Arithmetic mean |\n| **P3** | Substitution exam | Written – Individual | Replaces lowest score |\n\n\u003cbr\u003e\n\n[**Final Grade:**]() Arithmetic mean of assessments.\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n# II - [class_2- Introduction - Data Mining With Python](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/86d9d9fbc56efdd0b8e377955c1c7abf8879b775/class_2%20-%20Introduction%20-%20Data%20Mining%20With%20Python)\n\n\u003cbr\u003e\n\n☞ [Access Booklet](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/blob/81e2951f73c87cf7c4396a36d48be92384b7b720/class_1-%20Introduction%20-%20Data%20Mining%20With%20Python/Book%20-%20Introd%20to%20Data%20Mining%20With%20Python.pdf)\n\n\n\u003cbr\u003e\n\n## [Example 1]()\n\n\u003cbr\u003e\n\n\nThe following sample lists the number of minutes that 60 cable TV users watched content from their package in the last two hours. Construct a frequency distribution with 8 classes and build a histogram.\n\n\u003cbr\u003e\n\n\n[Data]():\n\n```\n20, 55, 5, 64, 78, 49, 91, 87, 18, 83, 33, 39, 30, 31, 59, 85, 102, 24, 27, 28,\n92, 108, 98, 67, 85, 109, 48, 19, 32, 69, 24, 59, 6, 49, 116, 37, 92, 43, 101, 60,\n55, 107, 25, 33, 57, 25, 17, 49, 24, 101, 14, 45, 73, 120, 91, 2, 11, 47, 21, 38\n```\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n### [Step 1](): Determine Range and Number of Classes\n\n- Minimum value: 2\n- Maximum value: 120\n- Number of classes ($k$): 8 (given)\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n### [Step 2](): Calculate Class Width\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n$$\n\\huge\nw = \\left\\lceil \\frac{\\text{max} - \\text{min}}{k} \\right\\rceil = \\left\\lceil \\frac{120 - 2}{8} \\right\\rceil = 15\n$$\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n### [Step 3](): Construct Class Intervals (from minimum value)\n\n| Class Interval | Explanation |\n| :-- | :-- |\n| 2 - 16 | Starts from minimum 2 |\n| 17 - 31 | 16 + 1 to 31 |\n| 32 - 46 | Next range |\n| 47 - 61 | Next range |\n| 62 - 76 | Next range |\n| 77 - 91 | Next range |\n| 92 - 106 | Next range |\n| 107 - 121 | Covers maximum 120 |\n\n\u003cbr\u003e\n\n### [Step 4](): Frequency Distribution Table\n\n\u003cbr\u003e\n\n| Class Interval | Frequency |\n| :--: | :--: |\n| 2 - 16 | 5 |\n| 17 - 31 | 14 |\n| 32 - 46 | 8 |\n| 47 - 61 | 13 |\n| 62 - 76 | 5 |\n| 77 - 91 | 8 |\n| 92 - 106 | 6 |\n| 107 - 121 | 5 |\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n### [Step 5](): Calculate Midpoints for Each Class\n\n\u003cbr\u003e\n\n$$\n\\Huge\nx_i = \\frac{\\text{Lower limit} + \\text{Upper limit}}{2}\n$$\n\n\u003cbr\u003e\u003cbr\u003e\n\n| Class Interval | Midpoint ($x_i$) |\n| :-- | :-- |\n| 2 - 16 | 9 |\n| 17 - 31 | 24 |\n| 32 - 46 | 39 |\n| 47 - 61 | 54 |\n| 62 - 76 | 69 |\n| 77 - 91 | 84 |\n| 92 - 106 | 99 |\n| 107 - 121 | 114 |\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n### [Step 6](): Calculate Mean Using Frequency and Midpoints\n\n\u003cbr\u003e\n\n### [Mean](): ($\\bar{x}$) is calculated by:\n\n\u003cbr\u003e\u003cbr\u003e\n\n$$\n\\Huge\n\\bar{x} = \\frac{\\sum f_i x_i}{\\sum f_i}\n$$\n\n\u003cbr\u003e\u003cbr\u003e\n\n### [Where](): $f_i$ = frequency, $x_i$ = [Midpoint]().\n\n\n\u003cbr\u003e\n\n### [Calculate each product]():\n\n\u003cbr\u003e\n\n| Class Interval | $f_i$ | $x_i$ | $f_i \\times x_i$ |\n| :-- | :-- | :-- | :-- |\n| 2 - 16 | 5 | 9 | 45 |\n| 17 - 31 | 14 | 24 | 336 |\n| 32 - 46 | 8 | 39 | 312 |\n| 47 - 61 | 13 | 54 | 702 |\n| 62 - 76 | 5 | 69 | 345 |\n| 77 - 91 | 8 | 84 | 672 |\n| 92 - 106 | 6 | 99 | 594 |\n| 107 - 121 | 5 | 114 | 570 |\n\n\u003cbr\u003e\n\n### [Sum frequencies](): $5 + 14 + 8 + 13 + 5 + 8 + 6 + 5$ = [64]()\n\n### [Sum of products](): $45 + 336 + 312 + 702 + 345 + 672 + 594 + 570$ = [3576]()\n\n\u003cbr\u003e\n\n### [Calculate mean]():\n\n\u003cbr\u003e\u003cbr\u003e\n\n$$\n\\huge\n\\bar{x} = \\frac{3576}{64} = 55.875\n$$\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n### [Step 7](): Histogram, Bar Plot and Time Series Frequency Distribution Over Time\n\n- Construct a histogram, bar plot and  Time Series  with class intervals on the x-axis and frequencies on the y-axis.\n- Each bar height corresponds to the frequency of the class.\n\n\n\u003cbr\u003e\n\n☞ [Access Code](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/blob/a61b0572e5bca4d6f06b0187722f8ef97214c0a4/class_1-%20Introduction%20-%20Data%20Mining%20With%20Python/Code/DataMining_1.ipynb)\n\n☞ [Access Dataset](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/blob/01b6e27e588c3b830561385f14bd0d246f55049d/class_1-%20Introduction%20-%20Data%20Mining%20With%20Python/Banks%20Dataset/banco.csv)\n\n☞ [Access Plots](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/a61b0572e5bca4d6f06b0187722f8ef97214c0a4/class_1-%20Introduction%20-%20Data%20Mining%20With%20Python/Plots)\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n###[Frequency Analysis and Time Series Visualization]()\n\nThis notebook demonstrates how to perform frequency analysis on a CSV dataset, visualize results with histograms and bar plots, and create a time series chart using Python.\n\n\u003cbr\u003e\n\n###  [1](). Install and Import Libraries\n\n```python\n# Import required libraries\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n```\n\n\u003cbr\u003e\n\n###  [2](). Load Dataset\n\n```python\n# Load CSV file (semicolon-separated)\ndf = pd.read_csv('chose your dataset', sep=';')\n\n# Select only the \"day\" column\ndf1 = df['day']\n```\n\n\u003cbr\u003e\n\n###  [3](). Calculate Frequencies\n\n\n```python\n# Calculate absolute frequency (ascending order)\nfreq_abs = pd.Series(df1).value_counts(ascending=True)\n\n# Calculate relative frequency (normalized, 3 decimal places)\nfreq_rel = pd.Series(df1).value_counts(normalize=True).round(3)\n\n# Create a DataFrame with both measures\ndf_freq = pd.DataFrame({\n    'Absolute Frequency': freq_abs,\n    'Relative Frequency': freq_rel\n})\n\n# Display the frequency table\ndisplay(df_freq)\n```\n\n\u003cbr\u003e\n\n###  [4]().  Histogram (Dark Theme)\n\n```python\n# Create figure and axes with dark background\nplt.style.use('seaborn-v0_8-darkgrid')\nfig, ax = plt.subplots(figsize=(16, 4))\nfig.patch.set_facecolor('black')\nax.set_facecolor('black')\n\n# Plot histogram\nsns.histplot(df1, color='turquoise', ax=ax)\n\n# Customize labels and ticks\nplt.xlabel(\"Values\")\nplt.ylabel(\"Frequency\")\nplt.title(\"Frequency Distribution\", color='white')\nplt.tick_params(axis='x', colors='white')\nplt.tick_params(axis='y', colors='white')\n\n# Show plot\nplt.show()\n```\n\n\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"1307\" height=\"386\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/48b994b0-6bf8-425d-8bc8-ecd7395c45c5\" /\u003e\n\n\u003cbr\u003e\n\n###  [5](). Bar Plot (Dark Theme)\n\n```python\n# Create figure and axes\nplt.style.use('seaborn-v0_8-darkgrid')\nfig, ax = plt.subplots(figsize=(10, 6))\nfig.patch.set_facecolor('black')\nax.set_facecolor('black')\n\n# Bar plot of absolute frequency\ndf_freq['Absolute Frequency'].plot(kind='bar', color=\"turquoise\", ax=ax)\n\n# Customize labels and ticks\nplt.xlabel(\"Values\")\nplt.ylabel(\"Frequency\")\nplt.title(\"Frequency Distribution\", color='white')\nplt.xticks(rotation=0, color='white')\nplt.yticks(color='white')\n\n# Show plot\nplt.show()\n```\n\n\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"842\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/6c28b3bf-1940-44e7-a4b3-80c03a736919\" /\u003e\n\n\u003cbr\u003e\n\n\n###  [6](). Time Series Preparation\n\n\n```python\n# Inspect available columns\nprint(df.columns)\n\n# Create a new DataFrame for time series analysis\ndf_time_series = df[['day', 'month']].copy()\n\n# Add dummy year (if year column is missing)\ndf_time_series['year'] = 2022\n\n# Convert to strings for concatenation\ndf_time_series['day'] = df_time_series['day'].astype(str)\ndf_time_series['year'] = df_time_series['year'].astype(str)\n\n# Create \"date\" column in dd-MMM-yyyy format\ndf_time_series['date'] = df_time_series['day'] + '-' + df_time_series['month'] + '-' + df_time_series['year']\ndf_time_series['date'] = pd.to_datetime(df_time_series['date'], format='%d-%b-%Y')\n\n# Set \"date\" as index\ndf_time_series = df_time_series.set_index('date')\n\n# Count occurrences per day\ndaily_counts = df_time_series.groupby(df_time_series.index).size()\n\n# Display first rows\ndisplay(daily_counts.head())\n```\n\n\u003cbr\u003e\n\n###  [7](). Time Series Plot (Dark Theme)\n\n\n```python\n# Set plot style\nplt.style.use('seaborn-v0_8-darkgrid')\nfig, ax = plt.subplots(figsize=(16, 6))\nfig.patch.set_facecolor('black')\nax.set_facecolor('black')\n\n# Plot time series\nplt.plot(daily_counts, color='turquoise')\n\n# Customize labels and ticks\nplt.title(\"Frequency Distribution Over Time\", color='white')\nplt.xlabel(\"Date\", color='white')\nplt.ylabel(\"Frequency\", color='white')\nplt.tick_params(axis='x', colors='white')\nplt.tick_params(axis='y', colors='white')\n\n# Show plot\nplt.show()\n```\n\n\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"1307\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/319298c8-04a3-4335-80e1-4b0c92fde027\" /\u003e\n\n\u003cbr\u003e\n\n### [Summary]()\n\nDummy Year: 2022 was used when year column was missing.\n\nVisualizations: Histograms, bar plots, and time series chart.\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n# III - [class_3 - Stats Review](https://github.com/Quantum-Software-Development/specialized-consulting-data-mining/tree/86d9d9fbc56efdd0b8e377955c1c7abf8879b775/class_3%20-%20Stats%20Review)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003cbr\u003e\u003cbr\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cbr\u003e\u003cbr\u003e\n\n\n\n\n##  [Bibliography]()\n\n\u003cbr\u003e\n\n### [Basic Bibliography]()\n\n- CASTRO, L. N. *Introdução a mineração de dados: conceitos básicos, algoritmos e aplicações*. Saraiva, 2016.  \n- PIRIM, H. *Recent Applications in Data Clustering*. IntechOpen, 2018.  \n- SEN, J. *Machine Learning: Artificial Intelligence*. IntechOpen, 2021.\n\n\u003cbr\u003e\n\n### [Complementary Bibliography]()\n\n- THOMAS, C. *Data Mining*. IntechOpen, 2018.  \n- HUTTER, F.; KOTTHOFF, L.; VANSCHOREN, J. *Automated Machine Learning: Methods, Systems, Challenges*. Springer Nature, 2019.  \n- NETTO, A.; MACIEL, F. *Python para Data Science e Machine Learning Descomplicado*. Alta Books, 2021.  \n- RUSSELL, S. J.; NORVIG, P. *Artificial Intelligence: A Modern Approach*. GEN LTC, 2022.  \n- SUD, K.; ERDOGMUS, P.; KADRY, S. *Introduction to Data Science and Machine Learning*. IntechOpen, 2020.\n\n\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n## 💌 [Let the data flow... Ping Me !](mailto:fabicampanari@proton.me)\n\n\u003cbr\u003e\u003cbr\u003e\n\n\n\n#### \u003cp align=\"center\"\u003e  🛸๋ My Contacts [Hub](https://linktr.ee/fabianacampanari)\n\n\n\u003cbr\u003e\n\n### \u003cp align=\"center\"\u003e \u003cimg src=\"https://github.com/user-attachments/assets/517fc573-7607-4c5d-82a7-38383cc0537d\" /\u003e\n\n\n\n\n\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e  ────────────── 🔭⋆ ──────────────\n\n\n\u003cp align=\"center\"\u003e ➣➢➤ \u003ca href=\"#top\"\u003eBack to Top \u003c/a\u003e\n\n\u003c!--\n\u003cp align=\"center\"\u003e  ────────────── ✦ ──────────────\n--\u003e\n\n\n\n\u003c!-- Programmers and artists are the only professionals whose hobby is their profession.\"\n\n\" I love people who are committed to transforming the world \"\n\n\" I'm big fan of those who are making waves in the world! \"\n\n##### \u003cp align=\"center\"\u003e( Rafael Lain ) \u003c/p\u003e   --\u003e\n\n#\n\n###### \u003cp align=\"center\"\u003e Copyright 2025 Quantum Software Development. Code released under the [MIT License license.](https://github.com/Quantum-Software-Development/Math/blob/3bf8270ca09d3848f2bf22f9ac89368e52a2fb66/LICENSE)\n","funding_links":["https://github.com/sponsors/Quantum-Software-Development","https://github.com/sponsors/Quantum-Software-Development/card"],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantum-software-development%2Fspecialized-consulting-data-mining","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantum-software-development%2Fspecialized-consulting-data-mining","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantum-software-development%2Fspecialized-consulting-data-mining/lists"}