{"id":21836903,"url":"https://github.com/waldohidalgo/freecodecamp_data_analysis_with_python_projects","last_synced_at":"2025-10-15T05:42:47.240Z","repository":{"id":264952130,"uuid":"894745486","full_name":"waldohidalgo/freecodecamp_data_analysis_with_python_projects","owner":"waldohidalgo","description":"Repositorio con los proyectos requisitos obligatorios para obtener la Data Analysis with Python Certification de Freecodecamp","archived":false,"fork":false,"pushed_at":"2024-11-30T17:34:40.000Z","size":965,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-26T10:11:47.310Z","etag":null,"topics":["data-analysis-with-python","dataanalysis","freecodecamp","freecodecamp-curriculum","freecodecamp-data-visualization","freecodecamp-project"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/waldohidalgo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-26T23:02:00.000Z","updated_at":"2024-11-30T17:34:44.000Z","dependencies_parsed_at":"2024-11-27T00:31:37.109Z","dependency_job_id":null,"html_url":"https://github.com/waldohidalgo/freecodecamp_data_analysis_with_python_projects","commit_stats":null,"previous_names":["waldohidalgo/freecodecamp_data_analysis_with_python_projects"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/waldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/waldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/waldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/waldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/waldohidalgo","download_url":"https://codeload.github.com/waldohidalgo/freecodecamp_data_analysis_with_python_projects/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244815958,"owners_count":20515023,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis-with-python","dataanalysis","freecodecamp","freecodecamp-curriculum","freecodecamp-data-visualization","freecodecamp-project"],"created_at":"2024-11-27T20:43:35.944Z","updated_at":"2025-10-15T05:42:47.163Z","avatar_url":"https://github.com/waldohidalgo.png","language":"Python","readme":"# Data Analysis with Python Projects\n\nRepositorio con el código solución a los 5 proyectos requisitos obligatorios para obtener la [Data Analysis with Python Certification](https://www.freecodecamp.org/learn/data-analysis-with-python/)\n\n## Tabla de Contenidos\n\n- [Data Analysis with Python Projects](#data-analysis-with-python-projects)\n  - [Tabla de Contenidos](#tabla-de-contenidos)\n  - [Certificación Obtenida](#certificación-obtenida)\n  - [Listado de Proyectos](#listado-de-proyectos)\n    - [1- Mean-Variance-Standard Deviation Calculator](#1--mean-variance-standard-deviation-calculator)\n      - [1.1- Proyecto Aprobado](#11--proyecto-aprobado)\n      - [1.2- Todos los tests superados](#12--todos-los-tests-superados)\n      - [1.3- Código Creado](#13--código-creado)\n    - [2- Demographic Data Analyzer](#2--demographic-data-analyzer)\n      - [2.1- Proyecto Aprobado](#21--proyecto-aprobado)\n      - [2.2- Todos los tests superados](#22--todos-los-tests-superados)\n      - [2.3- Código Creado](#23--código-creado)\n    - [3- Medical Data Visualizer](#3--medical-data-visualizer)\n      - [3.1- Proyecto Aprobado](#31--proyecto-aprobado)\n      - [3.2- Todos los tests superados](#32--todos-los-tests-superados)\n      - [3.3- Código Creado](#33--código-creado)\n      - [3.4- Gráficos Generados](#34--gráficos-generados)\n        - [3.4.1- Gráfico de Columnas](#341--gráfico-de-columnas)\n        - [3.4.2- Mapa de Calor](#342--mapa-de-calor)\n    - [4- Page View Time Series Visualizer](#4--page-view-time-series-visualizer)\n      - [4.1- Proyecto Aprobado](#41--proyecto-aprobado)\n      - [4.2- Todos los tests superados](#42--todos-los-tests-superados)\n      - [4.3- Código Creado](#43--código-creado)\n      - [4.4- Gráficos Generados](#44--gráficos-generados)\n        - [4.4.1- Gráfico de Líneas](#441--gráfico-de-líneas)\n        - [4.4.2- Gráfico de Barras](#442--gráfico-de-barras)\n        - [4.4.3- Box Plot](#443--box-plot)\n    - [5- Sea Level Predictor](#5--sea-level-predictor)\n      - [5.1- Proyecto Aprobado](#51--proyecto-aprobado)\n      - [5.2- Todos los tests superados](#52--todos-los-tests-superados)\n      - [5.3- Código Creado](#53--código-creado)\n      - [5.4- Gráfico Generado: Scatter Plot más Proyecciones](#54--gráfico-generado-scatter-plot-más-proyecciones)\n\n## Certificación Obtenida\n\nEl link para verificar la certificación es el siguiente: [Verificar Certificación en Freecodecamp](https://www.freecodecamp.org/certification/waldo-hidalgo/data-analysis-with-python-v7)\n\n![Certificación Obtenida](./data-analysis-with-python_certification.webp)\n\n## Listado de Proyectos\n\n### 1- Mean-Variance-Standard Deviation Calculator\n\n#### 1.1- Proyecto Aprobado\n\n![Primer Proyecto Aprobado](./Proyecto1_Mean-Variance-StandardDeviationCalculator/passed.webp)\n\n#### 1.2- Todos los tests superados\n\n![All tests passed](./Proyecto1_Mean-Variance-StandardDeviationCalculator/all_tests_passed.webp)\n\n#### 1.3- Código Creado\n\n```py\nimport numpy as np\n\ndef calculate(list):\n    n=len(list)\n    if n\u003c9:\n        raise ValueError(\"List must contain nine numbers.\")\n\n    orig=np.array(list)\n    reorg=orig.reshape(3,3)\n    mean=[np.mean(reorg,axis=0).tolist(),np.mean(reorg,axis=1).tolist(),np.mean(reorg)]\n    variance=[np.var(reorg,axis=0).tolist(),np.var(reorg,axis=1).tolist(),np.var(reorg)]\n    std=[np.std(reorg,axis=0).tolist(),np.std(reorg,axis=1).tolist(),np.std(reorg)]\n    maxv=[np.max(reorg,axis=0).tolist(),np.max(reorg,axis=1).tolist(),np.max(reorg)]\n    minv=[np.min(reorg,axis=0).tolist(),np.min(reorg,axis=1).tolist(),np.min(reorg)]\n    sumv=[np.sum(reorg,axis=0).tolist(),np.sum(reorg,axis=1).tolist(),np.sum(reorg)]\n\n    calculations={\n        'mean':mean,\n        'variance':variance,\n        'standard deviation':std,\n        'max':maxv,\n        'min':minv,\n        'sum':sumv\n    }\n\n    return calculations\n```\n\n### 2- Demographic Data Analyzer\n\nEl archivo CSV utilizado llamado **adult.data.csv** NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: [Link a Archivo](https://github.com/freeCodeCamp/boilerplate-demographic-data-analyzer/blob/main/adult.data.csv)\n\n#### 2.1- Proyecto Aprobado\n\n![Segundo Proyecto Aprobado](./Proyecto2_DemographicDataAnalyzer/passed.webp)\n\n#### 2.2- Todos los tests superados\n\n![All tests passed](./Proyecto2_DemographicDataAnalyzer/all_test_passed.webp)\n\n#### 2.3- Código Creado\n\nEl código que he creado va después de los comentarios. Cada comentario se refiere a lo que se pide realizar.\n\n```py\ndef calculate_demographic_data(print_data=True):\n    # Read data from file\n    df = pd.read_csv(\"adult.data.csv\")\n\n    # How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.\n    race_count = df['race'].value_counts()\n\n    # What is the average age of men?\n    average_age_men = round(df[df.sex=='Male'].age.mean(),1)\n\n    # What is the percentage of people who have a Bachelor's degree?\n    percentage_bachelors = round(((df.education[df.education==\"Bachelors\"].count())/(df.education.count()))*100,1)\n\n    # What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?\n    # What percentage of people without advanced education make more than 50K?\n\n    # with and without `Bachelors`, `Masters`, or `Doctorate`\n    higher_education = df[df.education.isin(['Bachelors', 'Masters', 'Doctorate'])]\n    lower_education = df[~df.education.isin(['Bachelors', 'Masters', 'Doctorate'])]\n\n    # percentage with salary \u003e50K\n    higher_education_rich = round((higher_education.salary[higher_education.salary=='\u003e50K'].count()/higher_education.salary.count())*100,1)\n    lower_education_rich = round((lower_education.salary[lower_education.salary=='\u003e50K'].count()/lower_education.salary.count())*100,1)\n\n    # What is the minimum number of hours a person works per week (hours-per-week feature)?\n    min_work_hours = round(df['hours-per-week'].min(),1)\n\n    # What percentage of the people who work the minimum number of hours per week have a salary of \u003e50K?\n    num_min_workers = round((df.loc[df['hours-per-week']==df['hours-per-week'].min(),['salary']].value_counts())['\u003e50K'],1)\n\n    rich_percentage = round((df.loc[df['hours-per-week']==df['hours-per-week'].min(),['salary']].value_counts(normalize=True)*100)['\u003e50K'],1)\n\n    # What country has the highest percentage of people that earn \u003e50K?\n    highest_earning_country = df.groupby(['native-country', 'salary']).size().unstack(fill_value=0).apply(lambda x: (x / x.sum()) * 100, axis=1)['\u003e50K'].idxmax()\n    #otra forma pd.crosstab()\n    highest_earning_country_percentage =round(df.groupby(['native-country', 'salary']).size().unstack(fill_value=0).apply(lambda x: (x / x.sum()) * 100, axis=1)['\u003e50K'].max(),1)\n\n    # Identify the most popular occupation for those who earn \u003e50K in India.\n    top_IN_occupation = df.loc[(df.salary=='\u003e50K')\u0026 (df['native-country']=='India'),['occupation']].mode().iloc[0,0]\n\n    # DO NOT MODIFY BELOW THIS LINE\n\n    if print_data:\n        print(\"Number of each race:\\n\", race_count)\n        print(\"Average age of men:\", average_age_men)\n        print(f\"Percentage with Bachelors degrees: {percentage_bachelors}%\")\n        print(f\"Percentage with higher education that earn \u003e50K: {higher_education_rich}%\")\n        print(f\"Percentage without higher education that earn \u003e50K: {lower_education_rich}%\")\n        print(f\"Min work time: {min_work_hours} hours/week\")\n        print(f\"Percentage of rich among those who work fewest hours: {rich_percentage}%\")\n        print(\"Country with highest percentage of rich:\", highest_earning_country)\n        print(f\"Highest percentage of rich people in country: {highest_earning_country_percentage}%\")\n        print(\"Top occupations in India:\", top_IN_occupation)\n\n    return {\n        'race_count': race_count,\n        'average_age_men': average_age_men,\n        'percentage_bachelors': percentage_bachelors,\n        'higher_education_rich': higher_education_rich,\n        'lower_education_rich': lower_education_rich,\n        'min_work_hours': min_work_hours,\n        'rich_percentage': rich_percentage,\n        'highest_earning_country': highest_earning_country,\n        'highest_earning_country_percentage':\n        highest_earning_country_percentage,\n        'top_IN_occupation': top_IN_occupation\n    }\n```\n\n### 3- Medical Data Visualizer\n\nEl archivo CSV utilizado llamado **medical_examination.csv** NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: [Link a Archivo](https://github.com/freeCodeCamp/boilerplate-medical-data-visualizer/blob/main/medical_examination.csv)\n\n#### 3.1- Proyecto Aprobado\n\n![Tercer Proyecto Aprobado](./Proyecto3_MedicalDataVisualize/passed.webp)\n\n#### 3.2- Todos los tests superados\n\n![All tests passed](./Proyecto3_MedicalDataVisualize/all_tests_passed.webp)\n\n#### 3.3- Código Creado\n\nLa generación de cortes cambia el tipo de datos a categorico lo que provoca errores en los tests si no se modifica. Para evitar el cambio de tipo de datos he utilizado el método **where**.\n\n```py\n# 1\ndf = pd.read_csv(\"medical_examination.csv\")\n\n# 2\n# df['overweight'] = pd.cut(df['weight']/(df['height']/100)**2,bins=[0,25,np.inf],labels=[0,1])\ndf['overweight']=np.where(df['weight']/(df['height']/100)**2\u003c=25,0,1)\n\n# 3\n#df['gluc']=pd.cut(df['gluc'],bins=[0,1,np.inf],labels=[0,1])\n#df['cholesterol']=pd.cut(df['cholesterol'],bins=[0,1,np.inf],labels=[0,1])\n\ndf['gluc']=np.where(df['gluc'] \u003c= 1, 0, 1)\ndf['cholesterol']=np.where(df['cholesterol']\u003c=1,0,1)\n\n# 4\ndef draw_cat_plot():\n    # 5\n    variables=['active', 'alco', 'cholesterol', 'gluc', 'overweight', 'smoke']\n    df_cat = pd.melt(df,id_vars=['cardio'],value_vars=variables)\n\n\n    # 6\n    df_cat = df_cat.groupby([\"cardio\", \"variable\", \"value\"]).size().reset_index().rename(columns={0:'total'})\n\n    # 7\n\n    # 8\n    fig = sns.catplot(data=df_cat, x=\"variable\", y=\"total\", hue=\"value\", col=\"cardio\",  kind=\"bar\", height=4, aspect=1.5 ).figure\n\n\n    # 9\n    fig.savefig('catplot.png')\n    return fig\n\n\n# 10\ndef draw_heat_map():\n    f1=(df['ap_lo'] \u003c= df['ap_hi'])\n    f2=(df['height'] \u003e= df['height'].quantile(0.025))\n    f3=(df['height'] \u003c= df['height'].quantile(0.975))\n    f4=(df['weight'] \u003e= df['weight'].quantile(0.025))\n    f5=(df['weight'] \u003c= df['weight'].quantile(0.975))\n\n    # 11\n    df_heat = df[f1 \u0026 f2 \u0026 f3 \u0026 f4 \u0026f5]\n\n    # 12\n    corr = df_heat.corr()\n\n    # 13\n    mask=np.triu(np.ones(corr.shape), 0).astype(bool)\n\n    # 14\n    fig, ax =plt.subplots(figsize=(12, 6))\n\n    # 15\n\n    sns.heatmap(corr, mask=mask, annot=True, linewidths=0.5, ax=ax,cmap='inferno',fmt=\".1f\")\n\n    # 16\n    fig.savefig('heatmap.png')\n    return fig\n```\n\n#### 3.4- Gráficos Generados\n\n##### 3.4.1- Gráfico de Columnas\n\n![Gráfico de Columnas](./Proyecto3_MedicalDataVisualize/catplot.png)\n\n##### 3.4.2- Mapa de Calor\n\n![Mapa de Calor](./Proyecto3_MedicalDataVisualize/heatmap.png)\n\n### 4- Page View Time Series Visualizer\n\nEl archivo CSV utilizado llamado **fcc-forum-pageviews.csv** NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: [Link a Archivo](https://github.com/freeCodeCamp/boilerplate-page-view-time-series-visualizer/blob/main/fcc-forum-pageviews.csv)\n\n#### 4.1- Proyecto Aprobado\n\n![Cuarto Proyecto Aprobado](./Proyecto4_PageViewTimeSeriesVisualizer/passed.webp)\n\n#### 4.2- Todos los tests superados\n\n![All tests passed](./Proyecto4_PageViewTimeSeriesVisualizer/all_tests_passed.jpg)\n\n#### 4.3- Código Creado\n\n```py\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom pandas.plotting import register_matplotlib_converters\nimport calendar\nimport numpy as np\nregister_matplotlib_converters()\nnp.float = float\n# Import data (Make sure to parse dates. Consider setting index column to 'date'.)\ndf = pd.read_csv(\"fcc-forum-pageviews.csv\",index_col='date',parse_dates=['date'])\n\n# Clean data\nf1=df.value\u003edf.value.quantile(0.025)\nf2=df.value\u003cdf.value.quantile(0.975)\ndf = df[f1 \u0026 f2]\n\n\ndef draw_line_plot():\n    # Draw line plot\n    fig=plt.figure(figsize=(14, 6))\n    plt.plot(df.index,df.value,'r')\n    plt.grid(True)\n    plt.title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')\n    plt.xlabel('Date')\n    plt.ylabel('Page Views')\n\n    # Save image and return fig (don't change this part)\n    fig.savefig('line_plot.png')\n    return fig\n\ndef draw_bar_plot():\n    # Copy and modify data for monthly bar plot\n    aux = df.resample('M').mean().reset_index()\n\n    aux['year'] = aux['date'].dt.year\n    aux['month'] = aux['date'].dt.month\n\n    df_bar=aux.pivot(index='year', columns='month', values='value')\n    # Draw bar plot\n\n    fig, ax = plt.subplots(figsize=(13, 8))\n\n    colors = plt.cm.tab20c(np.linspace(0, 1, 12))\n\n    width = 0.5 / len(df_bar.columns)\n    x = np.arange(len(df_bar.index))\n    month_names = {i: calendar.month_name[i] for i in range(1, 13)}\n    for i, month in enumerate(df_bar.columns):\n        ax.bar(\n            x + i * width,\n            df_bar[month],\n            width=width,\n            label=f'{month_names[month]}',\n            color=colors[i]\n        )\n\n    ax.legend(title='Months', loc='upper left')\n    ax.set_xlabel('Years', fontsize=12)\n    ax.set_xticks(x + width * (len(df_bar.columns) - 1) / 2)\n    ax.set_xticklabels(df_bar.index, fontsize=10)\n    ax.set_ylabel('Average Page Views',fontsize=12)\n    plt.tight_layout()\n\n    # Save image and return fig (don't change this part)\n    fig.savefig('bar_plot.png')\n    return fig\n\ndef draw_box_plot():\n    # Prepare data for box plots (this part is done!)\n    df_box = df.copy()\n    df_box.reset_index(inplace=True)\n    df_box['year'] = [d.year for d in df_box.date]\n    df_box['month'] = [d.strftime('%b') for d in df_box.date]\n\n    # Draw box plots (using Seaborn)\n\n    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))\n\n    cmap = plt.cm.tab20\n    colors = list(cmap(np.linspace(0, 1, len(df_box['year'].unique()))))\n\n    flierprops = dict(marker='o', color='black', markersize=7, markerfacecolor='red')\n\n    sns.boxplot(x='year', y='value',data=df_box, ax=ax1)\n    ax1.set_title('Year-wise Box Plot (Trend)')\n    ax1.set_xlabel('Year')\n    ax1.set_ylabel('Page Views')\n    ax1.grid(True, color='gray', linestyle='--', linewidth=0.5,dashes=(10, 5))\n\n    colors = list(cmap(np.linspace(0, 1, len(df_box['month'].unique()))))\n    months=list(calendar.month_abbr[1:])\n    sns.boxplot(data=df_box, x='month', y='value', ax=ax2, order=months, hue='month', palette=colors,flierprops=flierprops)\n    ax2.set_title('Month-wise Box Plot (Seasonality)')\n    ax2.set_xlabel('Month')\n    ax2.set_ylabel('Page Views')\n    ax2.grid(True, color='gray', linestyle='--', linewidth=0.5,dashes=(10, 5))\n\n    plt.tight_layout()\n\n    # Save image and return fig (don't change this part)\n    fig.savefig('box_plot.png')\n    return fig\n```\n\n#### 4.4- Gráficos Generados\n\n##### 4.4.1- Gráfico de Líneas\n\n![Gráfico de Líneas](./Proyecto4_PageViewTimeSeriesVisualizer/line_plot.png)\n\n##### 4.4.2- Gráfico de Barras\n\n![Gráfico de Barras](./Proyecto4_PageViewTimeSeriesVisualizer/bar_plot.png)\n\n##### 4.4.3- Box Plot\n\n![Box Plot](./Proyecto4_PageViewTimeSeriesVisualizer/box_plot.png)\n\n### 5- Sea Level Predictor\n\nEl archivo CSV utilizado llamado **epa-sea-level.csv** NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: [Link a Archivo](https://github.com/freeCodeCamp/boilerplate-sea-level-predictor/blob/main/epa-sea-level.csv)\n\n#### 5.1- Proyecto Aprobado\n\n![Quinto Proyecto Aprobado](./Proyecto5_SeaLevelPredictor/passed.webp)\n\n#### 5.2- Todos los tests superados\n\n![All tests passed](./Proyecto5_SeaLevelPredictor/all_tests_passed.jpg)\n\n#### 5.3- Código Creado\n\n```py\ndef draw_plot():\n    # Read data from file\n    df=pd.read_csv(\"epa-sea-level.csv\")\n\n    # Create scatter plot\n    plt.figure(figsize=(12, 6))\n    plt.scatter(df['Year'], df['CSIRO Adjusted Sea Level'], alpha=0.5,c='r')\n\n    # Create first line of best fit\n    res = linregress(df['Year'], df['CSIRO Adjusted Sea Level'])\n    years_extended=np.arange(df['Year'].min(), 2051)\n    plt.plot(years_extended, res.intercept + res.slope*years_extended, 'b', label='fitted line')\n\n    # Create second line of best fit\n    years_from_2000=np.arange(2000, 2051)\n    df_2000=df[df.Year\u003e=2000]\n    res_2000=linregress(df_2000.Year, df_2000['CSIRO Adjusted Sea Level'])\n    plt.plot(years_from_2000, res_2000.intercept + res_2000.slope*years_from_2000, 'g', label='2000 fitted line')\n\n    # Add labels and title\n    plt.grid(True, color='gray', linestyle='--', linewidth=0.2,dashes=(25, 25))\n    plt.xlabel('Year', fontsize=12)\n    plt.ylabel('Sea Level (inches)',fontsize=12)\n    plt.title('Rise in Sea Level')\n    plt.tight_layout()\n\n    y_proj_1 = res.intercept + res.slope * 2050\n    y_proj_2 = res_2000.intercept + res_2000.slope * 2050\n    plt.axvline(x=2050, color='purple', linestyle='--', linewidth=1, label='Projection Year (2050)')\n    plt.axhline(y=y_proj_1, color='blue', linestyle='--', linewidth=1)\n    plt.axhline(y=y_proj_2, color='green', linestyle='--', linewidth=1)\n\n\n    plt.text(2040, y_proj_1*1.025, f'{y_proj_1:.2f}', color='blue', va='center', fontsize=12,fontweight='bold')\n    plt.text(2037, y_proj_2*0.975, f'{y_proj_2:.2f}', color='green', va='center', fontsize=12,fontweight='bold')\n\n\n    # Save plot and return data for testing (DO NOT MODIFY)\n    plt.savefig('sea_level_plot.png')\n    return plt.gca()\n```\n\n#### 5.4- Gráfico Generado: Scatter Plot más Proyecciones\n\n![Gráfico Scatter Plot más Proyecciones](./Proyecto5_SeaLevelPredictor/sea_level_plot.png)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwaldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwaldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwaldohidalgo%2Ffreecodecamp_data_analysis_with_python_projects/lists"}