diff --git a/.gitignore b/.gitignore index 932f02d..c1591d7 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,167 @@ -/data/*.csv \ No newline at end of file +.DS_Store +### PyCharm ### +.idea + +### JupyterNotebooks ### +# gitignore template for Jupyter Notebooks +# website: http://jupyter.org/ + +.ipynb_checkpoints +*/.ipynb_checkpoints/* + +# IPython +profile_default/ +ipython_config.py + +# Remove previous ipynb_checkpoints +# git rm -r .ipynb_checkpoints/ + +### Python ### +# Byte-compiled / optimized / DLL files + +__pycache__/ +__init__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +pytestdebug.log + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +doc/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +# .env +.env/ +.venv/ +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pythonenv* + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# operating system-related files +# file properties cache/storage on macOS +*.DS_Store +# thumbnail cache on Windows +Thumbs.db + +# profiling data +.prof diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..54699cd --- /dev/null +++ b/Dockerfile @@ -0,0 +1,14 @@ +FROM python:3.9 + +WORKDIR /code + +COPY ./requirements.txt /code/requirements.txt + +RUN apt-get update && apt-get install -y procps \ + && pip install -U pip && rm /etc/localtime \ + && ln -s /usr/share/zoneinfo/America/Mexico_City /etc/localtime \ + && pip install -r ./requirements.txt + +COPY ./notebooks /code/notebooks + +CMD ["jupyter", "notebook", "--port=8888", "--ip=0.0.0.0", "--allow-root"] diff --git a/notebooks/Solucion Cristian.ipynb b/notebooks/Solucion Cristian.ipynb new file mode 100644 index 0000000..236e278 --- /dev/null +++ b/notebooks/Solucion Cristian.ipynb @@ -0,0 +1,8528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0365b4d8", + "metadata": {}, + "source": [ + "## Proyecciones de Natalidad en Jalisco (Python)\n", + "\n", + "El objetivo de este proyecto, es predecir la natalidad en el Estado de Jalisco para los años 2019 al 2021, utilizando los datos anuales del 2010 al 2018. Así como ofrecer una evaluación de los resultados." + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "ca4c78fc", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "from statsmodels.tsa.stattools import acf, pacf\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "from pmdarima.arima import auto_arima\n", + "import plotly.graph_objects as go\n", + "import matplotlib.pyplot as plt\n", + "import plotly.express as px\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import sqlite3" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "outputs": [ + { + "data": { + "text/plain": " Estado Total Hombres Mujeres No Especificado Año\n0 Aguascalientes 26583 13603 12980 0 2010\n1 Baja California 63559 32264 31295 0 2010\n2 Baja California Sur 13988 7076 6912 0 2010\n3 Campeche 20380 10230 10150 0 2010\n4 Coahuila de Zaragoza 56972 28879 28093 0 2010", + "text/html": "
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n
EstadoTotalHombresMujeresNo EspecificadoAño
0Aguascalientes26583136031298002010
1Baja California63559322643129502010
2Baja California Sur139887076691202010
3Campeche20380102301015002010
4Coahuila de Zaragoza56972288792809302010
\n
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "con: sqlite3.Connection = sqlite3.connect(\"file:./data/natalidad_en_mexico.sqlite?mode=ro\", uri=True)\n", + "df_nacimientos: pd.DataFrame = pd.read_sql_query(\"SELECT * from nacimientos\", con)\n", + "\n", + "display(df_nacimientos.head())\n", + "\n", + "con.close()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "76020529", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": " Estado Total Hombres Mujeres No Especificado Año\ncount 297 297 297 297 297 297\nunique 33 296 295 294 33 9\ntop Aguascalientes 61840 6777 12980 0 2010\nfreq 9 2 2 2 209 33", + "text/html": "
\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 \n \n \n \n \n
EstadoTotalHombresMujeresNo EspecificadoAño
count297297297297297297
unique33296295294339
topAguascalientes6184067771298002010
freq922220933
\n
" + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nacimientos.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "0d57875f", + "metadata": {}, + "source": [ + "## Overview de los datos\n", + "Se realiza un overview de los datos para verificar si existe o no algún dato o entrada nula.\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "f1d504aa", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 297 entries, 0 to 296\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Estado 297 non-null object\n", + " 1 Total 297 non-null object\n", + " 2 Hombres 297 non-null object\n", + " 3 Mujeres 297 non-null object\n", + " 4 No Especificado 297 non-null object\n", + " 5 Año 297 non-null object\n", + "dtypes: object(6)\n", + "memory usage: 14.0+ KB\n" + ] + }, + { + "data": { + "text/plain": "None" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df_nacimientos.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "f7df961b", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "Estado 0\nTotal 0\nHombres 0\nMujeres 0\nNo Especificado 0\nAño 0\ndtype: int64" + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nacimientos.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "c7fb5703", + "metadata": {}, + "source": [ + "#### Se puede corroborar que no existe ningún dato desaparecido. Sin embargo se puede observar que el tipo de dato de algunos campos es \"objeto\" y no un valor entero. Se realizará el respectivo cambio de tipo de variable.\n", + "_____" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 297 entries, 0 to 296\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Estado 297 non-null object\n", + " 1 Total 297 non-null int64 \n", + " 2 Hombres 297 non-null int64 \n", + " 3 Mujeres 297 non-null int64 \n", + " 4 No Especificado 297 non-null int64 \n", + " 5 Año 297 non-null int64 \n", + "dtypes: int64(5), object(1)\n", + "memory usage: 14.0+ KB\n" + ] + } + ], + "source": [ + "cols_num = ['Año', 'Hombres', 'Mujeres', 'Total', 'No Especificado']\n", + "df_nacimientos[cols_num] = df_nacimientos[cols_num].astype(int)\n", + "df_nacimientos.info()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Correlación entre columnas\n", + "Ahora se revisará si existe alguna correlación entre los datos, para ello se graficará la matriz de correlación de los datos datos.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 199, + "outputs": [ + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,8))\n", + "sns.heatmap(data=df_nacimientos[cols_num].corr(method='spearman'), vmax=1, center=0, annot=True, cmap='coolwarm', linewidths=0.5)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Análisis de la matriz de correlación\n", + "Se observa una obvio y total correlación entre el número `Total` de nacimientos y los nacimientos de `Hombres` y `Mujeres`, por lo que dichos campos/columnas (`Hombres` y `Mujeres`) serán eliminados y se considerará solamente el total de nacimientos por año. Respecto a la columna `No Especificado` aunque estadísticamente tenga una correlación cercana a cero (no correlacionado) se considerará como valores outlier debido al contexto, por lo que se optará por eliminar también." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 200, + "outputs": [], + "source": [ + "df_nacimientos.drop(columns=['Hombres', 'Mujeres', 'No Especificado'], inplace=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 201, + "outputs": [ + { + "data": { + "text/plain": " Estado Total Año\n0 Aguascalientes 26583 2010\n33 Aguascalientes 27427 2011\n66 Aguascalientes 26933 2012\n99 Aguascalientes 26434 2013\n132 Aguascalientes 27080 2014\n165 Aguascalientes 26486 2015\n198 Aguascalientes 26388 2016\n231 Aguascalientes 26955 2017\n264 Aguascalientes 25938 2018\n1 Baja California 63559 2010\n34 Baja California 65631 2011\n67 Baja California 62871 2012\n100 Baja California 61840 2013\n133 Baja California 63183 2014\n166 Baja California 63805 2015", + "text/html": "
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EstadoTotalAño
0Aguascalientes265832010
33Aguascalientes274272011
66Aguascalientes269332012
99Aguascalientes264342013
132Aguascalientes270802014
165Aguascalientes264862015
198Aguascalientes263882016
231Aguascalientes269552017
264Aguascalientes259382018
1Baja California635592010
34Baja California656312011
67Baja California628712012
100Baja California618402013
133Baja California631832014
166Baja California638052015
\n
" + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nacimientos.sort_values(['Estado', 'Año']).head(15)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "id": "b721a22a", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Visualización de los datos\n", + "\n", + "Se iniciará generando diagrama de caja para cada estado. Por temas de legilibilidad, se separaran en dos gráficos aparte." + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "outputs": [], + "source": [ + "# The list of states is separated in two different lists.\n", + "col_list = df_nacimientos.sort_values(['Total'])['Estado'].unique()\n", + "col_list_1 = col_list[:16]\n", + "col_list_2 = col_list[16:]\n", + "\n", + "# Create a new dataframe with the first list of states\n", + "df_1 = df_nacimientos[df_nacimientos['Estado'].isin(col_list_1)]\n", + "\n", + "# Create a new dataframe with the last list of states\n", + "df_2 = df_nacimientos[df_nacimientos['Estado'].isin(col_list_2)]" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 203, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "boxpoints": "all", + "hovertemplate": "Estado=%{x}
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Seguido de Aguascalientes, Baja California y Baja California Sur.\n", + "- Jalisco, *que es el estado de interés para este problema*, no tiene un comportamiento estacionario del todo, pero tampoco tiene variaciones tan marcadas como otros estados, por ejemplo México, Veracruz, Ciudad de México, Chiapas y San Luis Potosí.\n", + "____" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Ahora, se procederá a comprar el valor promedio anual de todos los estados, para ver qué estados tienen una tendencia promedio similar a la del estado de Jalisco.\n", + "____\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 205, + "outputs": [], + "source": [ + "# Grouping by state\n", + "df_grouped_state = df_nacimientos.groupby(by='Estado')\n", + "\n", + "# Calculating the mean value by state and sort the values in descending order\n", + "total_average_state = df_grouped_state['Total'].mean()\n", + 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### De las gráficas anteriores, se puede ver que los 3 estados tienen un comportamiento ligeramente parecido, y una tendencia a disminuir la cantidad de nacimientos por año. Sin embargo, los valores entre los que varían los estados de `Chiapas`y `Veracruz`son un mayores que los valores del estado de `Jalisco`. Esto lo pudimos corroborar con los diagramas de caja vistos anteriormente.Igualmente lo podemos ver con ayuda de la desviación estándar de cada conjunto de datos.\n", + "____" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 211, + "outputs": [ + { + "data": { + "text/plain": "(6175.9708953329755, 18888.362500345138, 16762.195772365598)" + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nacimientos_jalisco['Total'].std(), df_nacimientos_veracruz['Total'].std(), df_nacimientos_chiapas['Total'].std()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Como se puede ver, la desviación estándar del estado de `Jalisco` es entre 2.5 y 3 veces más pequeña que los otros dos estados.\n", + "\n", + "**Con base en lo anterior, se decidirá trabajar exclusivamente con los datos de Jalisco.**" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Modelo\n", + "\n", + "Para el desarrollo del modelo, se iniciará de la hipótesis que la serie de tiempo considerada, es una serie de tiempo estacionaria. Se propondrá un modelo ARMA (Autoregresivo con media móvil). Dicho modelo depende de dos parámetros fundamentales:\n", + "- **p**: *Cantidad de términos autoregresivos* y\n", + "- **q**: *Cantidad de términos media móvil*.\n", + "\n", + "El valor de $p$ se obtiene con ayuda de *la función parcial de autocorrelación*, mientras que el parámetro $q$ se calcula a parti de *la función de autocorrelación*. Se procede a graficar dichas funciones para la serie de tiempo considerada `df_nacimientos_jalisco`" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 212, + "outputs": [], + "source": [ + "df_nacimientos_jalisco.set_index('Año', inplace=True, drop=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 213, + "outputs": [], + "source": [ + "jalisco_acf = acf(df_nacimientos_jalisco, nlags=3)\n", + "jalisco_pacf = pacf(df_nacimientos_jalisco, nlags=3, method='ols')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 214, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "mode": "lines", + "y": [ + 1.0, + 0.6419837579254108, + 0.3918053820601308, + 0.05649269629942675 + ], + "type": "scatter" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "\n", + "fig.add_trace(go.Scatter(y=jalisco_acf, mode='lines'))\n", + "fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\")\n", + "fig.add_hline(y=CONFIDENCE_INTERVAL, line_dash=\"dash\", line_color=\"gray\")\n", + "fig.add_hline(y=-CONFIDENCE_INTERVAL, line_dash=\"dash\", line_color=\"gray\")\n", + "# fig.add_trace(go.Scatter(y=CONFIDENCE_INTERVAL, mode='lines'))\n", + "\n", + "fig.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "De las gráficas anteriores, se puede obtener los respectivos valores de los parámetros $p$ y $q$ en donde las gráficas cruzan el valor superior del intervalo de confianza. En ambos casos, dicho valor es de $p=q=1$. Por lo tanto, este valor será el que se utilizará para generar el modelo." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 216, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/cristian/Documents/personalProyects/interview-challenge/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:536: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency AS-JAN will be used.\n", + "\n", + "/Users/cristian/Documents/personalProyects/interview-challenge/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:536: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency AS-JAN will be used.\n", + "\n", + "/Users/cristian/Documents/personalProyects/interview-challenge/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:536: ValueWarning:\n", + "\n", + "No frequency information was provided, so inferred frequency AS-JAN will be used.\n", + "\n" + ] + }, + { + "data": { + "text/plain": "\n\"\"\"\n SARIMAX Results \n==============================================================================\nDep. Variable: Total No. Observations: 9\nModel: ARIMA(1, 0, 1) Log Likelihood -87.232\nDate: Tue, 11 Jan 2022 AIC 182.463\nTime: 04:54:34 BIC 183.252\nSample: 01-01-2010 HQIC 180.761\n - 01-01-2018 \nCovariance Type: opg \n==============================================================================\n coef std err z P>|z| [0.025 0.975]\n------------------------------------------------------------------------------\nconst 1.559e+05 7046.185 22.125 0.000 1.42e+05 1.7e+05\nar.L1 0.7630 0.552 1.381 0.167 -0.320 1.846\nma.L1 0.0990 0.685 0.144 0.885 -1.244 1.442\nsigma2 1.912e+07 1.712 1.12e+07 0.000 1.91e+07 1.91e+07\n===================================================================================\nLjung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 0.80\nProb(Q): 0.87 Prob(JB): 0.67\nHeteroskedasticity (H): 2.90 Skew: -0.41\nProb(H) (two-sided): 0.40 Kurtosis: 1.79\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n[2] Covariance matrix is singular or near-singular, with condition number 1.3e+23. Standard errors may be unstable.\n\"\"\"", + "text/html": "\n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n
SARIMAX Results
Dep. Variable: Total No. Observations: 9
Model: ARIMA(1, 0, 1) Log Likelihood -87.232
Date: Tue, 11 Jan 2022 AIC 182.463
Time: 04:54:34 BIC 183.252
Sample: 01-01-2010 HQIC 180.761
- 01-01-2018
Covariance Type: opg
\n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n
coef std err z P>|z| [0.025 0.975]
const 1.559e+05 7046.185 22.125 0.000 1.42e+05 1.7e+05
ar.L1 0.7630 0.552 1.381 0.167 -0.320 1.846
ma.L1 0.0990 0.685 0.144 0.885 -1.244 1.442
sigma2 1.912e+07 1.712 1.12e+07 0.000 1.91e+07 1.91e+07
\n\n\n \n\n\n \n\n\n \n\n\n \n\n
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 0.80
Prob(Q): 0.87 Prob(JB): 0.67
Heteroskedasticity (H): 2.90 Skew: -0.41
Prob(H) (two-sided): 0.40 Kurtosis: 1.79


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.3e+23. Standard errors may be unstable." + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": " Total\nAño \n2010-01-01 161543\n2011-01-01 163123\n2012-01-01 161217\n2013-01-01 159748\n2014-01-01 158592\n2015-01-01 151578\n2016-01-01 148377\n2017-01-01 151950\n2018-01-01 146960", + "text/html": "
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Total
Año
2010-01-01161543
2011-01-01163123
2012-01-01161217
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" + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_nacimientos_jalisco.reset_index(drop=False, inplace=True)\n", + "df_nacimientos_jalisco['Año'] = pd.to_datetime(df_nacimientos_jalisco['Año'], format='%Y')\n", + "df_nacimientos_jalisco.set_index('Año', inplace=True)\n", + "model = ARIMA(df_nacimientos_jalisco['Total'], order=(1, 0, 1))\n", + "result = model.fit()\n", + "display(result.summary())" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 224, + "outputs": [ + { + "data": { + "text/plain": "2019-01-01 148471.105742\n2020-01-01 150231.716437\n2021-01-01 151574.996315\nFreq: AS-JAN, Name: Predicciones Jalisco, dtype: float64" + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.predict(start='2019-01-01',end='2021-01-01').rename('Predicciones Jalisco')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "source": [ + "model = auto_arima(df_nacimientos_jalisco['Total'])\n", + "display(model.summary())" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 218, + "outputs": [ + { + "data": { + "text/plain": "\n\"\"\"\n SARIMAX Results \n==============================================================================\nDep. Variable: y No. Observations: 9\nModel: SARIMAX(1, 0, 0) Log Likelihood -87.337\nDate: Tue, 11 Jan 2022 AIC 180.675\nTime: 04:54:35 BIC 181.266\nSample: 0 HQIC 179.398\n - 9 \nCovariance Type: opg \n==============================================================================\n coef std err z P>|z| [0.025 0.975]\n------------------------------------------------------------------------------\nintercept 7697.5480 2.73e+04 0.282 0.778 -4.58e+04 6.12e+04\nar.L1 0.9501 0.179 5.312 0.000 0.600 1.301\nsigma2 9.92e+06 82.027 1.21e+05 0.000 9.92e+06 9.92e+06\n===================================================================================\nLjung-Box (L1) (Q): 0.07 Jarque-Bera (JB): 0.51\nProb(Q): 0.80 Prob(JB): 0.78\nHeteroskedasticity (H): 4.37 Skew: -0.23\nProb(H) (two-sided): 0.26 Kurtosis: 1.93\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n[2] Covariance matrix is singular or near-singular, with condition number 8.18e+20. Standard errors may be unstable.\n\"\"\"", + "text/html": "\n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n
SARIMAX Results
Dep. Variable: y No. Observations: 9
Model: SARIMAX(1, 0, 0) Log Likelihood -87.337
Date: Tue, 11 Jan 2022 AIC 180.675
Time: 04:54:35 BIC 181.266
Sample: 0 HQIC 179.398
- 9
Covariance Type: opg
\n\n\n \n\n\n \n\n\n \n\n\n \n\n
coef std err z P>|z| [0.025 0.975]
intercept 7697.5480 2.73e+04 0.282 0.778 -4.58e+04 6.12e+04
ar.L1 0.9501 0.179 5.312 0.000 0.600 1.301
sigma2 9.92e+06 82.027 1.21e+05 0.000 9.92e+06 9.92e+06
\n\n\n \n\n\n \n\n\n \n\n\n \n\n
Ljung-Box (L1) (Q): 0.07 Jarque-Bera (JB): 0.51
Prob(Q): 0.80 Prob(JB): 0.78
Heteroskedasticity (H): 4.37 Skew: -0.23
Prob(H) (two-sided): 0.26 Kurtosis: 1.93


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 8.18e+20. Standard errors may be unstable." + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "outputs": [ + { + "data": { + "text/plain": " Predicción Jalisco\n2019 147327.562462\n2020 147676.791857\n2021 148008.602592", + "text/html": "
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" + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = pd.DataFrame(model.predict(3), index=[\"2019\", \"2020\", \"2021\"], columns=['Predicción Jalisco'])\n", + "pred" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/readme.md b/readme.md index 151882f..5865420 100644 --- a/readme.md +++ b/readme.md @@ -1,46 +1,24 @@ -# Synx Challenge -Welcome to the Synx interview challenge! This repository is designed for candidates to fork and create [pull request](https://help.github.com/articles/about-pull-requests/) with their solutions. - -### What to expect -We understand that everyone's time is valuable, all challenges are designed to be short and concise way to explore skills we are interested in, in that way, we also try to offer a complete guide in how to install the needed environments and data (if required) and an extensive guide in how to submit results, so can focus on your soulution. - -In this challenge, we simplified the tools and processes that we actually use. It's ok not to be familiar with some of them, as the team we would like to be, we are here to help anyway we can, feel free to open an issue if you have any doubts. - -### What we are looking for? - -- **Data Science**: To undestand your own though process to solve problems, how do you use, process and present data. - -- **Technical Skills**: To assest your familiarity with tools and processes. - -# Challenge Instructions - -## Overview -We're going to leverage typical analysis and development workflows used by our team. In short, you'll: - -- Fork this repository and setup a clone on your machine. -- Run the Jupyter Notebooks by launcher the pre-configured Docker container or using [jupyter.org/try](https://jupyter.org/try) -- send us completed exam submittal via a [pull request PR](https://help.github.com/articles/about-pull-requests/) - -### Fork and Clone the Repo - -Fork this repo to your own account using the following button: - - -Fork - -Then using your command line shell, fork your repo: - -``` -git clone git@github.com:[GH_USERNAME]/interview-challenge.git -cd interview-challenge -``` - - -### Run Jupyter Locally - -If you have **Docker** [installed](https://docs.docker.com/get-docker/), - -``` -docker-compose up -``` +# Interview Challenge +En este repositorio se encuentra la solución planteada por `Cristian Zapata` al reto técnico presentado por +Synx.ia para la vacante de Científico de Datos de `Kapital Financiera` + +### ¿Cómo abrir la solución? + +Para abrir el `Jupyter-Notebook` con la solución, puede usar cualquiera de las siguientes opciones: + +- Si tiene **docker** instalado, puede seguir los siguientes pasos: + 1. En la terminal (`shell`) del sistema operativo en que se encuentre, muevase a la carpeta en donde clonó este repositorio. + 2. Dentro de la terminal, ejecute el siguiente comando: + `docker build ./ -t interview_cristian` el cuál construirá la respectiva imágen a partirt del `Dockerfile` configurado. + 3. Una vez termine la construcción de la imagen, ejecute el siguiente comando `docker run -p 8888:8888 interview_cristian` el cuál ejecutará el contenedor con la respectiva imagen anteriormente creada. + 4. Este último comando deberá inicializar un servidor de Jupyter localmente en el puerto 8888, copie la URL de acceso de la terminal a su navegador de preferencia. + 5. Una vez abierto Jupyter, navegue a la ruta `notebooks/Solucion Cristian.ipynb` para abrir el respectivo archivo con la solución planteada. + +- De igual manera, puede seguir las siguientes instrucciones: + 1. Crear un ambiente virtual de manera local, si no sabe cómo, puede seguir las [instrucciones aquí planteada](https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/26/python-virtual-env/). + 2. Una vez creado dicho ambiente virtual, desde la terminal y estando dentro del directorio de este repositorio, ejecute el siguiente comando: `pip install -r requirements.txt`. El cuál instalará las librerías necesarias para la solución planteada. + 3. Ejecute el comando `jupyter-notebook` en la terminal. + 4. Este último comando deberá inicializar un servidor de Jupyter localmente en el puerto 8888, copie la URL de acceso de la terminal a su navegador de preferencia. + 5. Una vez abierto Jupyter, navegue a la ruta `notebooks/Solucion Cristian.ipynb` para abrir el respectivo archivo con la solución planteada. + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..12031fa --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +seaborn==0.11.2 +matplotlib==3.3.2 +plotly==5.5.0 +jupyter==1.0.0 +pandas==1.3.5 +statsmodels==0.13.1 +pmdarima==1.8.4 \ No newline at end of file