|
2860 | 2860 | "file": "m_matplotlib/import" |
2861 | 2861 | }, |
2862 | 2862 | { |
2863 | | - "id": "mp_plot", |
| 2863 | + "id": "mp_chart", |
2864 | 2864 | "type": "function", |
2865 | 2865 | "level": 2, |
2866 | 2866 | "name": "Create chart", |
2867 | 2867 | "path": "visualpython - library - matplotlib - plot", |
2868 | 2868 | "desc": "", |
2869 | 2869 | "tag": "MATPLOTLIB, PLOT, CHART", |
2870 | | - "file": "m_matplotlib/plot" |
| 2870 | + "file": "m_visualize/Chart" |
2871 | 2871 | }, |
2872 | 2872 | { |
2873 | 2873 | "id": "mp_figure", |
|
3101 | 3101 | "icon": "apps/apps_style.svg" |
3102 | 3102 | } |
3103 | 3103 | }, |
3104 | | - { |
3105 | | - "id" : "pd_plot", |
3106 | | - "type" : "function", |
3107 | | - "level": 1, |
3108 | | - "name" : "Pandas", |
3109 | | - "tag" : "PANDAS PLOT,PANDAS", |
3110 | | - "path" : "visualpython - library - pandas - plot", |
3111 | | - "desc" : "Pandas plot creation", |
3112 | | - "file" : "m_library/m_pandas/plot", |
3113 | | - "apps" : { |
3114 | | - "color": 5, |
3115 | | - "icon": "apps/apps_visualize.svg" |
3116 | | - } |
3117 | | - }, |
3118 | | - { |
3119 | | - "id" : "visualize_chart", |
3120 | | - "type" : "function", |
3121 | | - "level": 1, |
3122 | | - "name" : "Matplotlib", |
3123 | | - "tag" : "MATPLOTLIB,CHART,VISUALIZATION,VISUALIZE", |
3124 | | - "path" : "visualpython - visualization - matplotlib", |
3125 | | - "desc" : "Matplotlib chart creation", |
3126 | | - "file" : "m_apps/Chart", |
3127 | | - "apps" : { |
3128 | | - "color": 5, |
3129 | | - "icon": "apps/apps_visualize.svg" |
3130 | | - } |
3131 | | - }, |
3132 | 3104 | { |
3133 | 3105 | "id" : "visualize_seaborn", |
3134 | 3106 | "type" : "function", |
|
3169 | 3141 | "icon": "apps/apps_dataset.svg" |
3170 | 3142 | } |
3171 | 3143 | }, |
3172 | | - { |
3173 | | - "id" : "ml_dataPrep", |
3174 | | - "type" : "function", |
3175 | | - "level": 1, |
3176 | | - "name" : "Data Prep", |
3177 | | - "tag" : "DATA PREPARATION,MACHINE LEARNING,ML", |
3178 | | - "path" : "visualpython - machine_learning - data prep", |
3179 | | - "desc" : "Data preparation for machine learning", |
3180 | | - "file" : "m_ml/DataPrep", |
3181 | | - "apps" : { |
3182 | | - "color": 6, |
3183 | | - "icon": "apps/apps_dataprep.svg" |
3184 | | - } |
3185 | | - }, |
3186 | 3144 | { |
3187 | 3145 | "id" : "ml_dataSplit", |
3188 | 3146 | "type" : "function", |
|
3198 | 3156 | } |
3199 | 3157 | }, |
3200 | 3158 | { |
3201 | | - "id" : "ml_evaluation", |
| 3159 | + "id" : "ml_dataPrep", |
3202 | 3160 | "type" : "function", |
3203 | 3161 | "level": 1, |
3204 | | - "name" : "Evaluation", |
3205 | | - "tag" : "PERFORMANCE EVALUATION,MACHINE LEARNING,ML", |
3206 | | - "path" : "visualpython - machine_learning - evaluation", |
3207 | | - "desc" : "Performance evaluation for machine learning", |
3208 | | - "file" : "m_ml/evaluation", |
| 3162 | + "name" : "Data Prep", |
| 3163 | + "tag" : "DATA PREPARATION,MACHINE LEARNING,ML", |
| 3164 | + "path" : "visualpython - machine_learning - data prep", |
| 3165 | + "desc" : "Data preparation for machine learning", |
| 3166 | + "file" : "m_ml/DataPrep", |
3209 | 3167 | "apps" : { |
3210 | 3168 | "color": 6, |
3211 | | - "icon": "apps/apps_evaluate.svg" |
| 3169 | + "icon": "apps/apps_dataprep.svg" |
| 3170 | + } |
| 3171 | + }, |
| 3172 | + { |
| 3173 | + "id" : "ml_autoML", |
| 3174 | + "type" : "function", |
| 3175 | + "level": 1, |
| 3176 | + "name" : "AutoML", |
| 3177 | + "tag" : "AUTO ML,MODEL,MACHINE LEARNING,ML", |
| 3178 | + "path" : "visualpython - machine_learning - automl", |
| 3179 | + "desc" : "AutoML model for machine learning", |
| 3180 | + "file" : "m_ml/AutoML", |
| 3181 | + "apps" : { |
| 3182 | + "color": 6, |
| 3183 | + "icon": "apps/apps_automl.svg" |
3212 | 3184 | } |
3213 | 3185 | }, |
3214 | 3186 | { |
|
3268 | 3240 | } |
3269 | 3241 | }, |
3270 | 3242 | { |
3271 | | - "id" : "ml_autoML", |
| 3243 | + "id" : "ml_evaluation", |
3272 | 3244 | "type" : "function", |
3273 | 3245 | "level": 1, |
3274 | | - "name" : "AutoML", |
3275 | | - "tag" : "AUTO ML,MODEL,MACHINE LEARNING,ML", |
3276 | | - "path" : "visualpython - machine_learning - automl", |
3277 | | - "desc" : "AutoML model for machine learning", |
3278 | | - "file" : "m_ml/AutoML", |
| 3246 | + "name" : "Evaluation", |
| 3247 | + "tag" : "PERFORMANCE EVALUATION,MACHINE LEARNING,ML", |
| 3248 | + "path" : "visualpython - machine_learning - evaluation", |
| 3249 | + "desc" : "Performance evaluation for machine learning", |
| 3250 | + "file" : "m_ml/evaluation", |
3279 | 3251 | "apps" : { |
3280 | 3252 | "color": 8, |
3281 | | - "icon": "apps/apps_automl.svg" |
| 3253 | + "icon": "apps/apps_evaluate.svg" |
3282 | 3254 | } |
3283 | 3255 | } |
3284 | 3256 | ] |
|
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