Merge branch 'main' into lab2

merge main into lab2

data/dollar.csv

@@ -0,0 +1,244 @@
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notebooks/lab2_1.ipynb

@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузка данных в DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"../data/kc_house_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Получение сведений о пропущенных данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df.isnull().any())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: {null_rate:.2f}%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/lab2_2.ipynb

@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузка данных в DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"../data/car_price_prediction.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Получение сведений о пропущенных данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df.isnull().any())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: {null_rate:.2f}%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

poetry.lock

@@ -467,6 +467,17 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.0"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cloudpickle-3.1.0-py3-none-any.whl", hash = "sha256:fe11acda67f61aaaec473e3afe030feb131d78a43461b718185363384f1ba12e"},
+    {file = "cloudpickle-3.1.0.tar.gz", hash = "sha256:81a929b6e3c7335c863c771d673d105f02efdb89dfaba0c90495d1c64796601b"},
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -675,6 +686,41 @@ files = [
 [package.extras]
 devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
 
+[[package]]
+name = "featuretools"
+version = "1.31.0"
+description = "a framework for automated feature engineering"
+optional = false
+python-versions = "<4,>=3.9"
+files = [
+    {file = "featuretools-1.31.0-py3-none-any.whl", hash = "sha256:87c94e9ae959c89acd83da96bd2583f3ef0f6daaa9639cbb6e46dbde2c742a18"},
+    {file = "featuretools-1.31.0.tar.gz", hash = "sha256:01bfb17fcc1715b4c3623c7bc94a8982122c4a0fa03350ed478601bb81f90155"},
+]
+
+[package.dependencies]
+cloudpickle = ">=1.5.0"
+holidays = ">=0.17"
+numpy = ">=1.25.0"
+packaging = ">=20.0"
+pandas = ">=2.0.0"
+psutil = ">=5.7.0"
+scipy = ">=1.10.0"
+tqdm = ">=4.66.3"
+woodwork = ">=0.28.0"
+
+[package.extras]
+autonormalize = ["autonormalize (>=2.0.1)"]
+complete = ["featuretools[dask,nlp,premium]"]
+dask = ["dask[dataframe] (>=2023.2.0)", "distributed (>=2023.2.0)"]
+dev = ["black[jupyter] (>=23.1.0)", "featuretools[dask,docs,test]", "pre-commit (>=2.20.0)", "ruff (>=0.1.6)"]
+docs = ["Sphinx (==5.1.1)", "autonormalize (>=2.0.1)", "click (>=7.0.0)", "featuretools[dask,test]", "ipython (==8.4.0)", "jupyter (==1.0.0)", "jupyter-client (>=8.0.2)", "matplotlib (==3.7.2)", "myst-parser (==0.18.0)", "nbconvert (==6.5.0)", "nbsphinx (==0.8.9)", "pydata-sphinx-theme (==0.9.0)", "sphinx-copybutton (==0.5.0)", "sphinx-inline-tabs (==2022.1.2b11)"]
+nlp = ["nlp-primitives (>=2.12.0)"]
+premium = ["premium-primitives (>=0.0.3)"]
+sklearn = ["featuretools-sklearn-transformer (>=1.0.0)"]
+sql = ["featuretools-sql (>=0.0.1)", "psycopg2-binary (>=2.9.3)"]
+test = ["boto3 (>=1.34.32)", "composeml (>=0.8.0)", "graphviz (>=0.8.4)", "moto[all] (>=5.0.0)", "pip (>=23.3.0)", "pyarrow (>=14.0.1)", "pympler (>=0.8)", "pytest (>=7.1.2)", "pytest-cov (>=3.0.0)", "pytest-timeout (>=2.1.0)", "pytest-xdist (>=2.5.0)", "smart-open (>=5.0.0)", "urllib3 (>=1.26.18)"]
+tsfresh = ["featuretools-tsfresh-primitives (>=1.0.0)"]
+
 [[package]]
 name = "flask"
 version = "3.0.3"
@@ -833,6 +879,20 @@ files = [
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
 ]
 
+[[package]]
+name = "holidays"
+version = "0.60"
+description = "World Holidays Framework"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "holidays-0.60-py3-none-any.whl", hash = "sha256:d857949c5ee35655215a10c5a26e6a856bdc3beccc4fbbc8debef98dfba17b82"},
+    {file = "holidays-0.60.tar.gz", hash = "sha256:495fc5123f5d92b92673237375eb8c15a03d21c647b089db509b7d9612267556"},
+]
+
+[package.dependencies]
+python-dateutil = "*"
+
 [[package]]
 name = "httpcore"
 version = "1.0.5"
@@ -914,6 +974,25 @@ examples = ["keras (>=2.4.3)", "matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "seab
 optional = ["keras (>=2.4.3)", "pandas (>=1.0.5)", "tensorflow (>=2.4.3)"]
 tests = ["black (>=23.3.0)", "flake8 (>=3.8.2)", "keras (>=2.4.3)", "mypy (>=1.3.0)", "pandas (>=1.0.5)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "tensorflow (>=2.4.3)"]
 
+[[package]]
+name = "importlib-resources"
+version = "6.4.5"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
+    {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
+
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
@@ -2708,6 +2787,11 @@ files = [
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
     {file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
     {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
@@ -2939,6 +3023,27 @@ files = [
     {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.67.0"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "traitlets"
 version = "5.14.3"
@@ -3110,7 +3215,33 @@ files = [
     {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
 ]
 
+[[package]]
+name = "woodwork"
+version = "0.31.0"
+description = "a data typing library for machine learning"
+optional = false
+python-versions = "<4,>=3.9"
+files = [
+    {file = "woodwork-0.31.0-py3-none-any.whl", hash = "sha256:5cb3370553b5f466f8c8599b1bf559584dc0b798cc1f2da26bbd7029d256c6f9"},
+    {file = "woodwork-0.31.0.tar.gz", hash = "sha256:6ef82af1d5b6525b02efe6417c574c810cfdcc606cb266bd0d7fb17a1d066b67"},
+]
+
+[package.dependencies]
+importlib-resources = ">=5.10.0"
+numpy = ">=1.25.0"
+pandas = ">=2.0.0"
+python-dateutil = ">=2.8.2"
+scikit-learn = ">=1.1.0"
+scipy = ">=1.10.0"
+
+[package.extras]
+complete = ["woodwork[updater]"]
+dev = ["click (>=8.1.7)", "pre-commit (>=2.20.0)", "ruff (>=0.1.6)", "woodwork[docs,test]"]
+docs = ["Sphinx (==5.1.1)", "ipython (==8.4.0)", "jupyter (==1.0.0)", "myst-parser (==0.18.0)", "nbconvert (==6.5.0)", "nbsphinx (==0.8.9)", "pyarrow (>=14.0.1)", "pydata-sphinx-theme (==0.9.0)", "sphinx-copybutton (==0.5.0)", "sphinx-inline-tabs (==2022.1.2b11)"]
+test = ["boto3 (>=1.34.32)", "moto[all] (>=5.0.0)", "pyarrow (>=14.0.1)", "pytest (>=7.0.1)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=2.1.0)", "smart-open (>=5.0.0)"]
+updater = ["alteryx-open-src-update-checker (>=3.1.0)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "a7e3d516bde2d6e4173d8a9770fb5337a0c806dadaeda355084b262c1995f7ea"
+content-hash = "09433ce7624fd6af995c85e9e980c57cd417491975e280f0a844931df35e5085"

pyproject.toml

@@ -17,8 +17,12 @@ apiflask = "^2.2.0"
 flask-cors = "^5.0.0"
 scikit-learn = "^1.5.2"
 imbalanced-learn = "^0.12.3"
+featuretools = "^1.31.0"
 
 
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.5"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"