|
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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